Apparatus and methodologies for personal health analysis

ABSTRACT

Apparatus and methodologies are provided for receiving and analyzing physical, behavioral, emotional, social, demographic and/or environmental information about an individual or a group to generate subscores indicative of the information, and utilizing the subscores to estimate or predict the overall wellness of the individual or group. More specifically, the present application relates to the use of physical, behavioral and environmental information about an individual or a group, at least some of the information being obtained and adapted from wearable devices, to measure, monitor and manage the individual&#39;s or group&#39;s health.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application claims the benefit of claim of U.S. provisionalapplication 62/324,746, filed Apr. 19, 2016, the entirety of which isincorporated herein by reference.

TECHNICAL FIELD

Apparatus and methodologies are provided for receiving and analyzingphysical, behavioral, emotional, social, demographic and/orenvironmental information about an individual or a group to generatesubscores indicative of the information, and utilizing the subscores toestimate or predict the overall wellness of the individual or group.More specifically, the present application relates to the use ofphysical, behavioral and environmental information about an individualor a group, at least some of the information being obtained and adaptedfrom wearable devices, to measure, monitor and manage the individual'sor group's health.

BACKGROUND

Methods and systems for monitoring the health of individuals are knownand can be used by individuals and health care providers to managedisease, improve healthcare quality, reduce health-care costs, and tooptimize the delivery of healthcare services. For instance, byindividualizing information about clients, health care providers canoffer customized care, proactively improve health, and perhaps evenenable individuals to take control of their own healthcare. Employersmay also have effective tools for managing and preventing the onset ofcomputer and sedentary work-related fatigue, stress and illness,reducing absenteeism, short and long-term disability costs. Further, therecent access to, and popularity of, wearable health-tracking devices,having improved sensors for obtaining health-related biometricinformation, provide the opportunity to significantly increase theefficacy and ease of individualized healthcare systems and theinteraction between an individual and the healthcare provider.

Current methods of monitoring health are limited to obtaining ormeasuring an individual's health information, such as heart rate,average steps taken per day, family history of disease, etc. However,such methods merely provide raw information which must be furtherprocessed or manipulated in order to arrive at meaningful conclusionsregarding the individual's health and disease risk.

Moreover, although mobile devices (e.g. wearable devices) have enabledindividuals to measure and obtain information regarding their personalhealth easily and on demand, the devices themselves are limited topresenting relatively simple information such as steps taken, minutes ofphysical activity in a given time frame, heart rate, etc. without moresophisticated information such as the client's disease risk orhealthiness.

As such, current methods of obtaining and analyzing data regarding onlythe individual's personal health information and biometrics provides alimited view into the actual health and disease risk of the individual.

There is a need for improved methodologies of obtaining, measuring, andmonitoring an individual's biometric and health information, and foraccurately processing such information into valuable indicators of theindividual's wellness, disease risk predictors, and other actionableinformation. Additionally, there is a need for improved methodologies ofproviding such wellness and disease risk indicators in comparison tohealth data from the general population to provide a contextualizedassessment of an individual's health. It is desirable that such a systembe operable without requiring the identification of pre-determinedconditions or pre-identified risk factors. Further, there is a need forimproved methodologies of providing such wellness indicators, diseasepredictors, and actionable information to clients on demand on a varietyof devices.

SUMMARY

Apparatus and methodologies for estimating or predicting the overallwellness of an individual or group of individuals is provided, providingcustomizable and personalized risk assessments of various health-relatedconditions, including the costs and/or financial impacts of the varioushealth-related conditions. The present system may be adapted to receiveincoming wellness information from a variety of sources, suchinformation including, without limitation, physical, behavioural,social, demographic, and environmental information. The system may beautomated and may be operative to analyze the incoming wellnessinformation, and to benchmark the information against datarepresentative of a corresponding distribution of the generalpopulation, to generate output information representing the individualor group of individual's wellness information.

In some embodiments, computer-implemented methods for determiningwellness in an individual or group of individuals is provided, themethod comprising providing a processor, in electronic communicationwith at least one or more device adapted to receive and transmitspecific incoming wellness information about the individual or group ofindividuals, providing a general population information database, inelectronic communication with the processor, for receiving andtransmitting general population information to the processor, andreceiving, at the processor, the specific incoming wellness informationand the general population information, and processing same to generateat least one digital biomarker subscore (e.g. “Health Subscore(s)”)indicative of the individual's wellness according to the specificwellness information, as compared against the general populationinformation, and generating at least one output (e.g. graphicalrepresentation) of the at least one digital subscore and transmittingthe output to the at least one or more devices. Preferably, some or allof the information may be sourced and adapted from at least one wearabledevice.

In some embodiments, the incoming wellness information may comprisevarious types of information including, but not limited to, physical,behavioral, emotional, social, demographic and/or environmentalinformation about the individual or group of individuals. The specificincoming wellness information may comprise information selected fromage, gender, height and weight, waist circumference, physical activity,minutes of moderate/vigorous activity, sleep patterns, smoking habits,drug and alcohol consumption, nutrition, family history, pain, stressand happiness levels, resting heart rate, exercise heart rate, heartrate variability, presence of pre-existing disease, job type,geo-location, EEG, voice data, breathing data, blood biometrics, bodycomposition (DXA), and aerobic fitness (VO2max).

Preferably, in some embodiments, the digital biomarkers generated hereinmay be indicative of, at least, health behaviors, chronic disease risk,mental health, or mortality. The health behaviors may compriseinformation about, at least, steps taken per day, moderate to vigorousactivity levels, sleep patterns, body mass index, waist circumference,smoking habits, drinking habits, nutritional habits, and aerobicfitness. Disease risk may comprise information about, at least,cardiovascular disease, diabetes, arthritis, lung disease, and pain. Themental health may comprise information about, at least, stress levels,happiness levels, depression, and model-based happiness. The mortalitysubscore may comprise information about, at least, mortality ratesassociated with one or more of the health behaviors, disease risk,and/or mental health subscores such as, at least, age, risk ofcardiovascular disease, and risk of diabetes. In some embodiments, thedigital biomarker subscores may be generated in an interactive manner,wherein the individual or group of individuals may predict or estimatehow various changes to the biomarker subscores changes their overallwellness (e.g. “What If” Tool). In some other embodiments, the digitalbiomarker subscores may be generated in an interactive manner, whereinthe individual or group of individuals may observe the digital biomarkersubscores of other individuals or groups of individuals for interactiontherewith (e.g. “People Like Me” Tool).

In some embodiments, the present computer-implemented methods mayfurther comprise processing at least one or more of the digitalbiomarker subscores against further general population information togenerate an overall wellness score (e.g. “VivaMe Score”) for theindividual or group of individuals. Preferably, the present systems areoperative to simultaneously and continuously generate both digitalbiomarker subscores and overall wellness scores, and to update eachaccording to feedback and machine learning systems, such updatingfurther incorporating information from the general population databaseand updating said database.

In some embodiments, a computer-implemented system for determining thewellness of an individual is provided, the system comprising at leastone device adapted to receive and transmit incoming wellness informationabout the individual, at least one general population database,operative to receive and transmit incoming wellness information from theat least one device an at least one processor, and at least oneprocessor, in electronic communication with the at least one device andthe general population database, the processor operative to receive theincoming wellness information from the at least one device and thegeneral population information from the database, and to process theinformation to generate at least one digital biomarker subscoreindicative of the individual's wellness according to the specificincoming wellness information as compared against the general populationinformation, and to generate at least one output indicative of the atleast one digital biomarker subscore and transmitting the output to theat least one device.

In some embodiments, the incoming wellness information and the generalpopulation information are transmitted via wired or wireless signaling.The incoming wellness information may be received and transmitted by theat least one device automatically, manually, or a combination thereof.The incoming wellness information may be received and transmitted by theat least one device intermittently, continuously, or a combinationthereof. In some embodiments, the at least one device may comprise, atleast, any device having a user interface, cloud computing, orapplication program interfaces. Preferably, the at least one device maycomprise one or more wearable devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of the present system according toembodiments herein;

FIG. 2 is an illustrative flowchart further demonstrating the presentsystem according to embodiments herein;

FIG. 3 provides an example of overall wellness information (e.g. aVivaMe Score) generated by the present computer-implemented systems, assuch information may be displayed to a user;

FIG. 4A illustrates exemplary options available to a user where a HealthSubscore or VivaMe score is found to be within a healthy, or optimalrange, according to embodiments herein;

FIG. 4B illustrates exemplary options available to a user where,according to some embodiments, it is desirable to set a target overallwellness score, and possible behavioral modifications that could be madein an attempt of achieving the target;

FIG. 5 shows an example plot of values estimating where a user's averagedaily steps ranks among the average daily steps of a correspondinggeneral population;

FIG. 6 shows an example plot of values estimating where a user's StepSubscore (Sstp) ranks according to the general population, the StepSubscore relating to the value of the steps contributor indicator, whichdetermines whether the Steps Subscore may or may not be used todetermine the user's overall wellness score;

FIGS. 7A, 7B, and 7C each show an example plots of curve functionsrelating to a user's time spent sleeping and their sleep score whereusers are less than or 65 years of age (FIG. 7A), a user's time spentsleeping and their sleep score where users are over 65 (FIG. 7B), andthe user's BMI (FIG. 7C);

FIG. 8 provides a Table summarizing some factors relative to the SmokingHealth Subscore, according to embodiments herein;

FIG. 9 provides a Table summarizing exemplary distribution data relatingto smokers in the general population, according to embodiments herein;

FIG. 10 provides a Table summarizing some factors relative to theDrinking (Alcohol) Health Subscore, according to embodiments herein;

FIG. 11 provides a Table summarizing exemplary distribution datarelating to estimated VO2max of a general population of age groups andgenders, according to embodiments herein;

FIG. 12 provides a Table summarizing exemplary distribution datarelating to resting heart rate in a general population, according toembodiments herein;

FIG. 13 provides a Table summarizing exemplary distribution datarelating to predicted VO2max of a general population, according toembodiments herein;

FIG. 14 provides a Table summarizing some example estimated parametersrelating to the VO2Max, according to embodiments herein;

FIG. 15 provides a Table summarizing some example incoming wellnessinformation used to generate Disease Risk Subscores (CardiovascularDisease), according to embodiments herein;

FIG. 16 shows an example pattern of the curve function to be applied tothe average risk of cardiovascular diseases to obtain a cardiovasculardisease Health Subscore according to embodiments herein;

FIG. 17 provides a Table summarizing some example incoming wellnessinformation used to generate Disease Risk Subscores (Diabetes),according to embodiments herein;

FIG. 18 shows an example pattern of the curve function to be applied tothe average risk of diabetes disease to obtain a diabetes HealthSubscore according to embodiments herein;

FIG. 19 provides a Table summarizing some example incoming wellnessinformation used to generate Stress Subscores, according to embodimentsherein;

FIG. 20 provides a Table summarizing some example incoming wellnessinformation used to generate Happiness Level Subscores, according toembodiments herein;

FIG. 21 provides a Table summarizing some example happiness levels ofthe general population given various values of average daily steps,average daily MV, and BMI;

FIG. 22 provides a Table summarizing some example general populationinformation regarding life expectancy, according to embodiments herein;

FIG. 23 provides a Table summarizing some example general populationinformation regarding mortality rates, according to embodiments herein;and

FIG. 24 provides a Table summarizing some example general populationinformation relating to the probabilities of dying for various ageranges.

DESCRIPTION OF THE EMBODIMENTS

Apparatus and methodologies for estimating or predicting the overallwellness of an individual or a group of individuals is provided,providing customizable and personalized risk assessments of varioushealth-related conditions. Various types of wellness information aboutthe individual or group may be sourced including, without limitation,physical, behavioral, social, demographic, and environmentalinformation, whereby the information is standardized and benchmarkedagainst data representative of relevant distribution of the generalpopulation. Some or all of the information may be sourced from at leastone device operative to collect and transmit the wellness informationsuch as, for example, mobile devices and/or wearable devices.

As will be described in more detail, the present computer-implementedsystems may collect and analyze wellness information about the user(s)to determine the user's wellness according to specific health-relatedmetrics (e.g. “Health Subscores”), as such specific metrics compare tothe general population, and then utilizes some or all of the specificmetrics to determine the user's overall wellness (e.g. “VivaMe Score”).As such, the present system may simultaneously generate both at leastone specific Health Subscore as well as an overall wellness VivaMescore, each being automatically and continuously compared to similarinformation about a corresponding distribution of the generalpopulation. Once generated, each Health Subscore(s) and VivaMe Score maybe processed into at least one form of output information displayed tothe user at their at least one device(s), the output information being,for example, a graphical representation indicative of the HealthSubscore(s) and VivaMe Scores, respectively.

As will also be described in more detail, in some embodiments, thepresent computer-implemented systems may further provide an interactivegoal-setting “What If” tool, operative to generate predictiveinformation about how an individual may impact their own wellness. Insome other embodiments, the present systems may further be operative toenable users to view the overall wellness information of other users,and to communicate and interact with such users, pursuant to a “PeopleLike Me” tool. The present apparatus and methodologies will now bedescribed in more detail having regard to the FIGS., Tables, andExamples provided.

Herein, the terms “individual”, “group”, “user” or “client” may be usedinterchangeably to describe at least one end-user of the presentsystems, and may be used to refer to those whose overall wellness isbeing assessed. The present apparatus and methodologies may be utilizedby an individual or by a group of individuals. The users need not sufferfrom any pre-determined or pre-existing condition, nor be categorizedinto any pre-identified risk factor group. Indeed, such individuals maybe healthy individuals desiring to maintain or increase their overallwellness. The users may also be individuals or groups that have beendiagnosed with one or more pre-existing health conditions/health-relatedfactors. It should further be understood that the present systems may beutilized on individuals or groups of individuals of any age, including,for example, children, adolescents, adults, and senior citizens.

The term “wellness information” may be used to collectively refer tovarious forms of information about an individual or group of individualsthat can be collected from a variety of sources and analyzed, asdescribed in more detail herein. Without limitation, wellnessinformation may include, at least, physical, behavioral, emotional,social, demographic, environmental information, or any combinationthereof, about the individual or the group. It is contemplated that atleast some of the wellness information may be obtained, directly orindirectly, from one or more wearable devices.

The term “Health Subscore” may be used to describe, in part, the user'swellness according to specific health-related metrics, as compared to acorresponding cohort of the general population. Health Subscore(s), alsoreferred to herein as digital biomarker score(s), may be generated bythe present system utilizing some or all of incoming wellnessinformation collected including, without limitation, individualizedinformation about, at least, the individual's age, gender, height andweight (BMI), waist circumference, physical activity, sleep patterns,smoking habits, drug and alcohol consumption, nutrition, family history,pain, stress and happiness levels, resting heart rate, exercise heartrate, heart rate variability, presence of pre-existing disease, jobtype, geo-location, electroencephalogram (EEG), voice data, breathingdata, blood biometrics, body composition (DXA), aerobic fitness (VO2max)and other variables defined by the individual or health care provider,etc. As will be described, the individualized information may bestandardized and compared to a distribution of general populationinformation corresponding to the user. The generated Health Subscoresmay be divided into three broad categories, namely, health behaviors,disease risk, and mental health, and presented to the users in a mannerrepresentative of their wellness in specific health-related categories(e.g. numerical value). As will be described, one or more of thegenerated Health Scores may be used to further process the user'soverall wellness (e.g. “VivaMe Scores”).

The terms “overall wellness”, “overall health”, “VivaMe Score” may beused interchangeably to describe, at least, the user's overall wellness,as compared to a corresponding cohort of the general population. VivaMeScores may be generated by the present system utilizing some or all ofthe generated Health Subscores(s) to determine, at least, the user'sphysical or mental health or wellbeing, overall risk of physical ormental disease, and/or mortality. By way of example, the present VivaMeScores may provide information (prediction or estimations) about,without limitation, risk of heart disease (e.g. congestive heartfailure, heart attack, coronary heart disease, angina), Diabetes (e.g.adult onset, Type 2), arthritis or osteoarthritis (inflamed joints),lung disease (including asthma, chronic bronchitis, emphysema), bodilypain (e.g. lower back pain) and mortality, overall mental wellbeing(e.g. risk of depression, overall happiness), VO2max and aerobic fitnesslevels. As will be described, the generated Health Subscores may beprocessed and compared to a distribution of general populationinformation corresponding to the user (e.g. age, gender, etc). Forexample, as will be described, overall VivaMe or VivaHealth Scores,denoted as S, may be generated from the weighted average of one or moreof the at least one Health Subscore(s). Once generated, the one or moreVivaMe Scores may be presented to the users in a manner representativeof their overall wellness (e.g. graphical representation, numericalvalue, or other appropriate indication).

Herein the term “general population information” or “generaldistribution data” may refer to general population information obtainedfrom a database of corresponding information about the generalpopulation, such information serving as a standardized baseline forcomparative purposes. General population information varies dependingupon the individual or group utilizing the present systems, and/or thespecific health subscore or overall wellness score being generated forthe individual or group.

Herein the term “devices” may generally be used to refer any appropriatedevices, processors, or network paradigms operative to collect, transmitand/or receive information such as, in this case, wellness information,and to customizably (and interactively) display the system-generatedwellness results back to the user. By way of example, “devices” may beany appropriate technologies known in the art including, withoutlimitation, devices operative to transmit or receive information viawired or wireless signaling, via a plurality of user interfaces (e.g.desktop computers, notebook computers, laptop computers, mobile devicessuch as cellphones and tablets), via cloud computing, via applicationprogram interfaces (“API”), or via wearable devices, or the like. Hereinthe term “wearable devices” may refer to wearable technology, commonlyreferred to “wearables”, including electronic technologies or computersthat can be incorporated into items of clothing or accessories that cancomfortably be worn on the body (e.g. heart rate monitors, smartwatches, Fitbits™, Garmins™, API, medical devices, etc.). It should beunderstood that wearables are operative to perform many of the samecomputing tasks as mobile phones, laptops or other portable electronicdevices (e.g. sensory and scanning features, such as biofeedback andtracking of physiological function). It should also be understood thatthe present wearable devices further comprise some form of data-inputcapability, data-storage capability, and data communication capability,operative to transmit information in real time. Wearables may include,without limitation, watches, glasses, contact lenses, e-fabrics, smartfabrics, headbands, head gear (scarves, caps, beanies), jewelry, etc.

As above, the present apparatus and methodologies will now be describedin more detail having regard to the Figures, Tables, and Examplesprovided.

Generally, having regard to FIG. 1, the present computer-implementedsystem 10 can be used to collect wellness information about anindividual or group to determine, predict or estimate wellness.According to embodiments herein, the wellness information may include,at least, one form of physical, behavioral, emotional, social,demographic, and/or environmental information about the individual orgroup. Incoming wellness information may be collected and received bythe system automatically, manually (i.e. such as input by the individualor a health care provider), or a combination thereof. Incoming wellnessinformation may be collected and received intermittently, continuously,or a combination thereof. Incoming wellness information may be receivedpassively or actively, and may be collected over short or long durationsof time (e.g., over a 7-day period or longer).

As shown, the present system 10 may collect the wellness informationfrom at least one device 12 a, 12 b, . . . 12 n, the devices beingprogrammed to automatically and/manually measure and receive wellnessinformation, and to transmit the incoming information to the presentsystem for processing. Such transmission may be via any appropriatemeans known it the art including, without limitation, via wired orwireless signaling, or via a plurality of user interfaces including,without limitation, desktop computers, notebook computers, laptopcomputers, mobile devices such as cellphones and tablets, or wearabledevices such as heart rate monitors, smart watches, Fitbits™, Garmins™,medical devices, API, etc. In some embodiments, the one or more devices12 n may comprise wearables having at least one sensor operative tomeasure and record wellness information about the user(s). Wellnessinformation may be collected using software programs through anyappropriate means including, without limitation, apps for Android™ andiOS™, executable files for Windows™ or OSX™, or through an internetwebpage, etc.

By way of example, incoming wellness information may include informationrelating to general health-related conditions or metrics such as,without limitation, age, gender, height and weight (Body Mass Index;i.e., height/cm and weight/kg), waist circumference, physical activity(e.g., daily or average step-count, bouts of activity in variousintensity ranges, changes in activity patterns over time, types ofactivity, frequency of activity, sitting time, standing time, sedentarytime etc), minutes of moderate/vigorous activity, sleep patterns (totalsleep time, time spent in each sleep stage, number of sleepinterruptions), smoking habits, drug and alcohol consumption (e.g.,frequency/quantity), general nutrition, family history, pain, stress andhappiness levels, resting heart rate, exercise heart rate, heart ratevariability, presence of pre-existing disease, job type, geo-location,EEG, voice data, breathing data, blood biometrics, body composition(DXA), aerobic fitness (VO2max) and other variables defined by theindividual or health care provider, etc.

Incoming wellness information may be transmitted from the devices 12 nvia a network 100, such as the Internet, to at least one server (orprocessor) 110 for processing. Servers 110, in electronic communicationwith devices 12 n are operative to collect, analyze and store theincoming wellness information. Servers 110, in further electroniccommunication with at least one general information database 120, mayfurther be operative to collect, analyze and store general populationinformation from the general information database 120. As above, Servers110 may be programmed to receive wellness information and generalpopulation information, and to process the information using a suite ofalgorithms to simultaneously generate at least one Health Subscore andVivaMe score(s). Once generated, each of the Health Subscores and VivaMescores may be transmitted back to the user at their at least one device12 n.

More specifically, having regard to FIG. 2, an exemplary flowchart ofthe present system is provided. As described, wellness information maybe collected from least one or more devices adapted to measure andtransmit wellness information about an individual or a group ofindividuals. Wellness information may be transmitted from the at leastone device(s) (Step 201), and/or by the user(s) (Step 202). Some or allof the wellness information may be received from the devices and/orindividuals manually, and/or some or all of the wellness information maybe received automatically as, for example, according to a schedule (e.g.continuously, or intermittently over a predetermined segments of time;Step 203).

Incoming wellness information may be transmitted to the present one ormore servers via a network (Step 204), the servers being operative toreceive and store the information (Step 205) for processing. The one ormore servers also being operative collect, analyze, and store healthdata about the general population from a general population database(Step 206). The database may, in turn, be operative to collect, analyze,and store health data regarding the general population from the network,such as the Internet (Step 207). As above, the present server may beprogrammed to update the wellness information using the generalpopulation information, and vice versa, so as to maintain continuouslyupdated wellness information and database of general populationinformation used to generate the present wellness scores. Once thewellness information and the general population information is received,the servers may process the information (as described in more detailbelow) using a suite of algorithms to simultaneously generate at leastone Health Subscore and VivaMe score(s) (Step 208). Each of the HealthSubscores and VivaMe Scores may be calculated continuously,periodically, or upon receipt of a request, from time to time, by theuser(s) (Step 209). Once calculated, the generated Health Subscores andVivaMe scores can then be transmitted via the network back to theuser(s) (Step 210) such as, for example, to the users' at least onedevice 12 n. Each generated Health Subscore and VivaMe score may beconverted into at least one form of output information such as, agraphical representation indicative of the Health Subscore and VivaMeScore, respectively, for display on the one or more device(s) (Step211). The network used to transmit the Health Subscore and VivaMe Scoreto the user can be the same network used to send the wellnessinformation and general health information to the present systems, or adifferent network. Wellness information collected and analyzed by thepresent system may be deleted or stored on the server and/or one or moredevice(s).

In some embodiments, the present systems receive, at the server orprocessor, wellness information about the individual user or group user,and general population information, and process the information togeneral at least one digital biomarker subscore indicative of the user'sspecific wellness information (e.g. “Health Subscore”). The at least oneHealth Subscore being generated may depend the information beingrequested, and upon the demographic, biometric and behavioralinformation being used. The at least one digital biomarker subscores maybe generated in a manner that can be interpreted by the user as beinghigh, low, or within a healthy range, as compared to correspondingwellness information about a predetermined cohort of the generalpopulation. In some embodiments, digital biomarker subscores may begenerated in a manner that suggests the individual or group ofindividuals could take certain actions to modify their behavior (e.g.increasing daily steps or reducing their alcohol/cigarette consumption),improving their subscores.

Having regard to FIG. 3, the present computer-implemented systems may,at the server or processor, further process one or more of the at leastone Health Subscores to generate an overall wellness score, or “VivaMeScore”, as compared to further corresponding wellness information aboutthe predetermined cohort of the general population. The VivaMe Scorebeing generated may depend the information being requested, and upon theat least one Health Subscore being used. As above, the VivaMe Score maybe automatically generated and/or manually requested, from time to time,by the user. The VivaMe Score may be generated in a manner that can beinterpreted by the user as being high, low, or within a healthy range,as compared to corresponding wellness information about a predeterminedcohort of the general population. It should be understood that thegeneral population information collected and analyzed to determine theone or more digital biomarker scores may or may not be the same generalpopulation information collected and analyzed to determine the overallwellness VivaMe Score.

It is an advantage of the present system that, according to embodimentsherein, general population information has been collected and processed,and is accessible for analysis purposes. General information data may beautomatically and continuously updated. In some embodiments, generalpopulation data can be obtained from a variety of resources including,without limitation, from publicly or privately available databases,international, national, or regional reports on health statistics, etc.(e.g. National Youth Fitness Survey Treadmill Examination Manual). Insome embodiments, general population data regarding the population ofthe client's current country of residence is used. Alternatively,population data of other countries or a combination of countries can beused.

Such general population data may be stored on a server, data cloud, orother centralized location, in a manner that enables multiple end-usersof the present system to access the general population information. Thepresent system thus avoids need to store general information datalocally on the end-user's device. The present system furtherconveniently provides a feedback component where the general populationinformation database 120 may be continuously and dynamically updatedwith wellness information collected from the end-users and theirdevices. Various methods of data storage and access can be used tocreate, update, and maintain the database of general population healthinformation, such as SQL, JPQL, Microsoft Excel™, and the like.According to embodiments herein, the general population data (i.e.population distribution information) accessed by the present processormay be organized, manipulated, and updated in any appropriate mannerknown in the art without departing from the scope of the presentinvention. The general population database may be automatically(continuously or intermittently) updated as new population data becomesavailable. Preferably, individual or group Health Subscores and/orVivaMe Scores can be fed back to the database, thereby periodically orcontinuously updating the general population with individual's or groupof individual's data.

Having regard to FIGS. 4A and 4B, it is an advantage that the presentsystem may simultaneously generate both a specific Health Subscore andan overall wellness VivaMe Score, each being generated by standardizingthe information and comparing the information to similar informationabout a corresponding distribution of the general population. It is afurther advantage that, once generated, each of the Health Subscore(s)and VivaMe Scores may be processed into at least one form of outputinformation displayed to the user, the output information being, forexample, a graphical representation indicative of the Health Subscoreand VivaMe Score, respectively. In some embodiments, the resultingoutput information may be attributed to an individual or group ofindividual's user's login ID, where applicable and available.Preferably, the present system enables users to create a user-specificprofile linked to information that can be stored on the one or moredevices (e.g. the user's personal devices and/or a server). Theinformation contained in the client profile can comprise, for example, aunique login ID for the client, a password, the client's age, gender,occupation, weight, height, family disease history, diagnosis of variousdiseases, average daily steps, average daily activity (moderate tovigorous, “MV”), and previously calculated subscores and overallwellness scores, if available. As such, the client's average dailysteps, average daily MV activity, and other biometric data linked to theprofile can periodically be updated automatically continuously orperiodically by the wearable or mobile device, and/or manually updatedby the client. A copy of the calculated scores, along with a date stamp,can be stored on the server and/or the one or more devices, and linkedto the client's login ID, such that the client can view the subscore,and the date it was calculated, at a later time.

Accordingly, the user, their employer, insurance company, or health-careprovider may obtain simple, personalized information about the user'swellness, and where the user ranks according the general population. Theinformation may be used to motivate the user to improve their overallwellness, or to enable the user or health care provider to customize thehealth or wellness plan for the user (FIG. 4A). Indeed, in someembodiments, the present system is operative to provide an interactivegoal-setting “What If” Tool (see FIG. 4B), operative to estimate orpredict how changes in behavior, personal characteristics, or specificsubscores could impact their overall health, personal risk of disease,mortality, etc. Where it is desirable to change one or more individualHealth Subscores (e.g. increasing daily steps or daily activity), acorresponding positive change in the overall VivaMe Scores may also beachieved. Accordingly, users or their health-care providers mayexperiment, set personal goals, or pose questions about how varyingcombinations of health behavior changes or changes in personal healthsubscores could impact their overall wellness score. For example, a usercould determine whether increasing their steps per day by 500 has moreimpact on the risk for diabetes than losing 5 lbs, or whether acombination of the two changes has the greatest overall positive impact.In other embodiments, the present system is operative to provideinteractive communication to other users via, for example, a “PeopleLike Me” Tool, enabling users similar in age, gender, job type, healthcondition, etc. to share their results and goals. The present system,therefore, may be utilized by an individual or a group of individuals toobtain optimized, accurate results about their overall health andwellbeing. In some embodiments, user(s) can also change incomingwellness information to determine how their overall scores may beaffected. In such a manner, the present system may provide customizable,on-demand, actionable health information to user(s).

The present systems may further be utilized to estimate or predict thecosts of various diseases and savings that could be associated withvarious behavioral changes or changes in personal characteristics, suchas increased physical activity or decreases in weight, providing theadvantage that the costs or financial implications of an individual's orgroup's overall wellness can be estimated or predicted, and improved.For example, the present system may be used by third parties other thanthe individual user, such as an employer evaluating a group ofemployees, a health-care provider, or an insurance provider or actuaryevaluating optimal insurance coverage, enabling the identification andprevention of risk factors or health-related concerns (e.g. underwritinginsurance programs) of an individual or within an entire group. Forexample, the present systems may be utilized to determine health risks,mortality, etc. and to more effectively assign or alter insuranceprograms or premiums, or to reward individuals based upon the generatedHealth Subscores and/or VivaMe Scores. The present system may further beused to evaluate the outcomes of health and/or wellness programs,enabling the creation and optimization of health-related programs andproducts, insurance programs and products, and wellness support programsand products. The present system may be operative to identify andaddress issues such as sedentary workers, absenteeism, and risk ofshort- and long-term disability (e.g., including mental health claimsand inability to cope with increased productivity demands). It iscontemplated that the present system may be used alone or in combinationwith other known social engagement services. Without limitation, thepresent apparatus and methodologies provide repeatable and valid outputinformation to the user, using personalized feedback to enable practicalgoal setting, and interactive wellness planning based upon health and/orfinancially-driven goals.

As such, without limitation, the present computer-implemented system maybe programmed to utilize various modeling techniques (e.g., Prediction,Estimation, etc.). In one embodiment, a Prediction Model may be usedwhere the user provides self-reported demographic information, withouttaking into account personalized heart rate data. Such a method may bepractical for large populations, or cases where heart rate is notmonitored. In other embodiments, an Estimation Model (resting heartrate) may be used where both demographic and heart rate information areprovided. Resting heart rate may be self-reported or measured by the atleast one device. Such a VO2max estimate could be passively calculatedusing heart rate data collected from at least one device. In yet anotherembodiment, an Estimation Model (heart rate and perceived exertion) maybe used to take into account heart rate and an accompanying rating ofperceived exertion from the user. In this case, the user could indicatethe rate of perceived exertion when prompted during exercise, orfollowing a workout. Such a model may only require one pair of heartrate and exertion to be accurate, but could increase in accuracy withthe addition or incorporation of more data. In yet another embodiment,an Estimation Model (treadmill test) may be used to take into accountheart rate recorded during a simply two-stage treadmill test, the testbeing customized for each user. In this case, the user may be promptedwith instructions for the test, and heart rate during the test is usedto calculate VO2max.

As stated above, Health Subscores and an overall VivaMe health scoresfor individuals or groups are generated using a suite of algorithms. Theinputs, functions, and outputs of the algorithms vary depending on thewellness category for which the subscore or overall wellness score isbeing generated. Below are Examples of the algorithms used, although itwould be understood that the algorithms described below are forexemplary purposes only, and modifications can be made thereto to refineand/optimize the present systems.

Health Subscores

By way example, specific wellness information, along with generalpopulation information, can be processed to generate at least onedigital biomarker subscore indicative of the specific wellnessinformation. Herein, specific wellness information, or Health Subscores,can be divided into three categories: Health Behaviors, Disease Risk,and Mental Health (VivaMind subscores).

Health Behaviors

Health Behavior scores may be calculated utilizing the client'sdemographic information, biometric data, and data regarding a client'shealth behaviors, as well as similar data of the general population orsegments thereof to which the client's data is compared.

Steps

By way of example, a steps subscore Sstp may be generated to indicatethe individual or group's wellness with respect to the average number ofsteps taken per day, based on how the individual or group ranks comparedwith the general population information. Input information forgenerating the steps subscore may comprise age (Clientage), gender(ClientGender), brand of fitness device (Device, if applicable), averagenumber of steps taken per day (StepDaily), the amount of daily steps theclient wishes to increase (IncrSteps), and a steps contributionindicator (D594), which is a yes/no value that determines whether thesteps subscore contributes to the calculation of the client's overallwellness score.

Age may be determined by the age of the client, or average age of thegroup. Gender may be selectable between unknown, male, and female. Thebrand of fitness device is the brand of the device used by the client totrack his/her steps and may be selectable as between, for example,Garmin™, Fitbit™, Misfit™, Actigraph™, Actical™, or others. The brand offitness device can be used to select an adjustment factor (Adjust) toapply to the client's average daily step count in order to account fordiscrepancies between the measured steps across the various possibledevices used by the users. The daily average steps taken per day is thenumber of steps taken by the client over several days, averaged acrossthe number of days measured. Alternatively, the client can input asubjective number for daily steps to be used by the algorithm.Distribution data of average daily steps taken by the general populationis provided to serve as a standardized baseline. The steps distributiondata can be grouped into age brackets 20-29, 30-39, 40-49, 50-59, 60-69,and 70+. For each age bracket and each value of gender (unknown, male,or female), 9 levels of deciles can be created (10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, and 90%). A curve function to be applied to the rankingamong the general population to calculate the Steps subscore, in thisembodiment a piecewise linear function, can be made by connecting asequence of 2-D points: (0,0), (16,25), (31,50), (50,62), (69,74),(84,86), (100,100). First, the average daily step count“ClientStepAvgActi” is calculated as follows:

ClientStepAvgActi=StepDaily+IncrSteps+Adjust

where “StepDaily” is the average daily steps measured by the client'sfitness device or reported by the client, “IncrSteps” is the number ofsteps the client wishes to increase his/her daily steps by, and “Adjust”is the adjustment factor to account for the brand of the client'sfitness device. While the adjustment factor is added to the steps inthis calculation, the method of adjustment can be changed as desired,for example by multiplying StepDaily by a weighting factor instead ofaddition, or not used at all.

The Steps Rank is then estimated based on the client's average dailysteps ClientStepAvgActi and the general population distribution data.The appropriate distribution data set is selected based on the client'sage and gender. Where the distribution data is only divided into 9levels of deciles (for each age/gender bracket), an additional twolevels can be created. For 0%, the quartile is simply set to zero, as noone can have a negative step count. The 100% quartile can be created byextending the 90% quartile by the average step difference between thesuccessive deciles in the distribution data.

Presuming that the distribution of steps between the deciles is asfollows:

Cumulative percent (%) 0 10 20 30 40 50 60 70 80 90 100 Steps quartiles₀ s₁ s₂ s₃ s₄ s₅ s₆ s₇ s₈ s₉ s₁₀

where s₀ . . . s₁₀ are the average daily steps of each quartile, s₀=0,and s₁₀=s₉+Σ_(i=1) ⁸(s_(i+1)−s_(i))/8. The rank of the client among thepopulation StepsRank can then be estimated using the following formula:

${StepsRank} = \left\{ {\begin{matrix}{r_{0} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{0}}{s_{1} - s_{0}} \right)}} & {{{if}\mspace{14mu} s_{0}} \leq {ClientStepAvgActi} < s_{1}} \\{r_{1} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{1}}{s_{2} - s_{1}} \right)}} & {{{if}\mspace{14mu} s_{1}} \leq {ClientStepAvgActi} < s_{2}} \\{r_{2} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{2}}{s_{3} - s_{2}} \right)}} & {{{if}\mspace{14mu} s_{2}} \leq {ClientStepAvgActi} < s_{3}} \\{r_{3} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{3}}{s_{4} - s_{3}} \right)}} & {{{if}\mspace{14mu} s_{3}} \leq {ClientStepAvgActi} < s_{4}} \\{r_{4} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{4}}{s_{5} - s_{4}} \right)}} & {{{if}\mspace{14mu} s_{4}} \leq {ClientStepAvgActi} < s_{5}} \\{r_{5} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{5}}{s_{6} - s_{5}} \right)}} & {{{if}\mspace{14mu} s_{5}} \leq {ClientStepAvgActi} < s_{6}} \\{r_{6} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{6}}{s_{7} - s_{6}} \right)}} & {{{if}\mspace{14mu} s_{6}} \leq {ClientStepAvgActi} < s_{7}} \\{r_{7} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{7}}{s_{8} - s_{7}} \right)}} & {{{if}\mspace{14mu} s_{7}} \leq {ClientStepAvgActi} < s_{8}} \\{r_{8} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{8}}{s_{9} - s_{8}} \right)}} & {{{if}\mspace{14mu} s_{8}} \leq {ClientStepAvgActi} < s_{9}} \\{r_{9} + {10 \times \left( \frac{{ClientStepAvgActi} - s_{9}}{s_{10} - s_{9}} \right)}} & {{{if}\mspace{14mu} s_{9}} \leq {ClientStepAvgActi} < s_{10}} \\r_{10} & {{{if}\mspace{14mu} x_{c}} \geq s_{10}}\end{matrix}.} \right.$

where r₀, r₁, r₂, . . . , r₁₀ are 0, 10, 20, . . . , 100, respectively.The formula can be plotted on as shown in FIG. 5. For convenience, anotation for the piecewise linear function, f_(c)(. ; .) is used, withRS={(s₀,r₀), (s₁,r₁), (s₂,r₂), . . . , (s₁₀,r₁₀)}. Then StepsRank abovecan be written as:

${StepsRank} = \left\{ \begin{matrix}{f_{c}\left( {{ClientStepAvgActi};{RS}} \right)} & {{{{if}\mspace{14mu} {ClientStepAvgActi}} \leq s_{10}};} \\100 & {{{{if}\mspace{14mu} {ClientStepAvgActi}} > s_{10}};}\end{matrix} \right.$

In general, suppose A={(x₀,y₀), (x₁,y₁). (x₂,y₂), . . . ,(x_(n),y_(n))}, where x₀<x₁<x₂< . . . <x_(n), then f_(c)(x; A) isdefined as:

${f_{c}\left( {x;A} \right)} = \left\{ \begin{matrix}{y_{0} + {\left( {y_{1} - y_{0}} \right) \times {\left( {x - x_{0}} \right)/\left( {x_{1} - x_{0}} \right)}}} & {{{{if}\mspace{14mu} x_{0}} \leq x < x_{1}};} \\{y_{1} + {\left( {y_{2} - y_{1}} \right) \times {\left( {x - x_{1}} \right)/\left( {x_{2} - x_{1}} \right)}}} & {{{{if}\mspace{14mu} x_{1}} \leq x < x_{2}};} \\\; & \vdots \\{y_{i} + {\left( {y_{i + 1} - y_{i}} \right) \times {\left( {x - x_{i}} \right)/\left( {x_{i + 1} - x_{i}} \right)}}} & {{{{if}\mspace{14mu} x_{i}} \leq x < x_{i + 1}};} \\\; & \vdots \\{y_{n - 1} + {\left( {y_{n} - y_{n - 1}} \right) \times {\left( {x - x_{n - 1}} \right)/\left( {x_{n} - x_{n - 1}} \right)}}} & {{{{if}\mspace{14mu} x_{n - 1}} \leq x < x_{n}};} \\y_{n} & {{{if}\mspace{14mu} x} = {x_{n}.}}\end{matrix} \right.$

Once StepsRank is determined, a curve function can be applied toStepsRank to obtain the Steps subscore S_(stp). In an embodiment, thecurve function can be a piecewise linear function, defined by: whereSC={(0,0),

$S_{stp} = \left\{ \begin{matrix}{{{{f_{e}\left( {{StepsRank};{SC}} \right)}\mspace{14mu} {if}\mspace{14mu} D\; 594} \leq {YES}};} \\{{{{NULL}\mspace{14mu} {if}\mspace{14mu} D\; 594} > {NO}},}\end{matrix} \right.$

(16,25), (31,50), (50,62), (69,74), (84,86), (100,100)} and D594 is thevalue of the steps contribution indicator, which determines whether theSteps subscore contributes to the calculation of the client's overallwellness score. A graphical representation of the Sstp as a function ofStepsRank when D594 =YES is shown in FIG. 6.

In some embodiments, the curve function may be defined by Sstp above,however, it would be appreciated that the curve function may be definedin any way desired. For example, S_(stp) can be equal to the StepsRankfor simplicity. Additionally, the curve can be changed to make mostclients' scores look better by setting the curve function to be aconcave function. Preferably, the two conditions of the curve functionthat should be satisfied are that first, it must be a non-decreasingfunction (assuming that the higher step count results in better health)and second, it must include the two points (0,0),(100,100).

The present systems are operative to discern the appropriate generalpopulation distribution data that should be selected given the client'sgender and age bracket in order to obtain the most accurate resultsregarding the client's steps subscore.

If D594=YES, the formula of S_(stp(AG)) is given by:

$S_{{stp}{({AG})}} = \left\{ \begin{matrix}66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\65 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\68 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\65 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\64 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\65 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\65 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\68 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}},}\end{matrix} \right.$

Otherwise, S_(stp(AG))=NULL. The above data are provided as an exampleof average scores of a Canadian population in various age and genderbrackets.

The steps subscore S_(stp) can be compared with the average scoreS_(stp(AG)) of the general population in the client's age and gendercategory to determine whether the client's subscore is better than,equal to, or worse than others in the same age and gender category. Aqualitative scale can be used to indicate whether the client's stepssubscore S_(stp) is excellent, very good, good, fair, or poor. In someembodiments, the subscores for each rating may be provided in a rangesuch as, for example: Excellent: 86-100; Very good: 74-85; Good: 62-73;Fair: 50-61; and Poor: 0-50. It should be appreciated that any otherscore may be utilized.

Moderate to Vigorous Activity (MV)

As another example, a level of moderate to vigorous activity subscoreS_(mv) may be generated to indicate the individual or group's wellnesswith respect to the average amount of time spent performing moderate tovigorous activity (i.e. MV) per day, based upon how the individual orgroup ranks compared to the general population. Input information forgenerating the MV subscore may comprise age (Clientage), gender(ClientGender), average number of minutes of moderate to vigorousactivity performed per day or, if the measured average daily MV is notavailable, the reported average daily MV from the client (MVDaily), theamount of daily MV the client wishes to increase (IncrMV), and a MVcontribution indicator (D595), which is ayes/no value that determineswhether the MV subscore contributes to the calculation of the client'soverall wellness score.

First, the client's average daily MV “ClientMVAvgActi” is calculated asfollows:

ClientMVAvgActi=MVDaily+IncrMV

where MVDaily is the average daily MV measured by the client's fitnessdevice or reported by the client, and IncrMV is the amount of daily MVthe client wishes to increase.

In some embodiments, the MV Rank can then be estimated based on theaverage daily MV and the appropriate distribution data of the averagedaily MV of the general population can be selected based on age andgender. As with the Steps subscore, general population distribution dataof MV can be provided to act as a baseline for the average daily MV ofthe general population. The MV distribution data can be grouped into thesame age/gender brackets as for the steps distribution data, eachbracket having 9 levels of deciles. Two additional levels of deciles canonce again be added to the existing 9levels of deciles in thedistribution data, such that the distribution of MV between deciles isas follows:

Cumulative percent (%) 0 10 20 30 40 50 60 70 80 90 100 MV quartile m₀m₁ m₂ m₃ m₄ m₅ m₆ m₇ m₈ m₉ m₁₀

where m₀ . . . m₁₀ are the average daily MV of each quartile, and m₀=0,m₁₀=m₉+Σ_(i=1) ⁸(m_(i+1)−,_(i))/8. The MVRank among the population canthen be estimated using the same formula used above for steps. As withthe steps subscore above, the client's MV subscore S_(mv) can becompared with the average score S_(mv(AG)) of the general population inthe client's age and gender category to determine whether the client'ssubscore is better than, equal to, or worse than others in the same ageand gender category. A qualitative scale can be used to indicate whetherthe client's MV subscore S_(mv) is excellent, very good, good, fair, orpoor. In some embodiments, the subscores for each rating may be providedin a range such as, for example: Excellent: 86-100; Very good: 74-85;Good: 62-73; Fair: 50-61; and Poor: 0-50. It should be appreciated thatany other score may be utilized.

Sleep

As another example, a sleep subscore S_(slp) may be generated toindicate the individual or group's sleep wellness with respect to theaverage number of hours of sleep per day, based upon a comparison to thesleep patterns of the general population distribution information. Asabove, input information may be age (Clientage), gender (ClientGender),and average number of hours of sleep per day (SleepDaily), eithermeasured by the client's own device or as reported by the client, theamount of daily sleeping time the client wishes to increase (IncrSleep),and a sleep contribution indicator (D596), which is a yes/no value thatdetermines whether the sleep subscore contributes to the calculation ofthe client's overall wellness score.

First, the client's average daily sleeping time (in hours)“NewClientSleep” is calculated as follows:

NewClientSleep=SleepDaily+IncrSleep/60

where SleepDaily is the average daily hours of sleep of the clientmeasured by the client's fitness device or reported by the client, andIncrSleep is the amount of daily hours of sleep the client wishes toincrease.

If the sleep contribution indicator D596= YES, then the client's Sleepsubscore S_(slp) can then be calculated by applying the curve functionf_(c) as shown by the following formula:

$S_{slp} = \left\{ \begin{matrix}{f_{c}\left( {{NewClientSleep};{SCS}_{1}} \right)} & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} \leq {65\mspace{14mu} {and}}} \\{{0 \leq {NewClientSleep} \leq 14};}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} \leq {65\mspace{14mu} {and}}} \\{{{NewClientSleep} > 14};}\end{matrix} \\{f_{c}\left( {{NewClientSleep};{SCS}_{2}} \right)} & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} > {65\mspace{14mu} {and}}} \\{{0 \leq {NewClientSleep} \leq 14};}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} > {65\mspace{14mu} {and}}} \\{{{NewClientSleep} > 14},}\end{matrix}\end{matrix} \right.$

where SCS₁-{(0,0), (6,62), (7,86), (7.5,100), (8.5,100), (9,86),(10,62), (14,0)}; and SCS₂={(0,0), (5,62), (7,100), (8,100), (9,62),(14,0)}.

If D596= NO, then a NULL value is returned for S_(slp). FIGS. 7A and 7Bdepict a graphical representation between sleeping time and sleepingscore is shown for individuals less than or 65 years of age (FIG. 7A)and over 65 (FIG. 7B).

If D596=YES, the formula of S_(slp(AG)) is given by:

$S_{{slp}{({AG})}} = \left\{ \begin{matrix}96 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\90 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\85 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\84 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\89 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\100 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\83 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\86 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\82 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\85 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\100 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\100 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\99 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\95 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\88 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\84 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\88 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\100 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}},}\end{matrix} \right.$

Otherwise, S_(slp(AG))=NULL. The above example data are average scoresof a Canadian population in various age and gender brackets.

The sleep health subscore S_(slp) can be compared with the average scoreS_(slp(AG)) of the general population in the same age and gendercategory to determine whether the client's subscore is better than,equal to, or worse than others in the same age and gender category. Aqualitative scale can be used to indicate whether the client's sleepsubscore S_(slp) is excellent, very good, good, fair, or poor. In someembodiments, the subscores for each rating may be provided in a rangesuch as, for example: Excellent: 86-100; Very good: 74-85; Good: 62-73;Fair: 50-61; and Poor: 0-50. It should be appreciated that any otherscore may be utilized.

BMI

As another example, Body Mass Index or “BMI” health subscore S_(bmi) maybe generated to indicate the individual or group's wellness with respectto BMI, based on how the individual or group ranks compared with thegeneral population information. The inputs are the client's age(Clientage), gender (ClientGender), height (ClientHeight—in cm orinches), weight (ClientWeight—in kg or lbs), the weight that clientintends to change in kg (IncrWeight), a BMI/weight contributionindicator (D597), which is a yes/no value that determines whetherBMI/weight is taken into the calculation of the client's overallwellness score, and a weight/BMI selector (B597) which is selectablebetween “weight” or “BMI” and indicates which one of BMI and weight ischosen. A BMI contribution indicator (IBMI) is “YES” if the BMI/weightindicator is “YES” and the weight/BMI selector is “BMI”. Additionally,ClientScaleHeight is chosen between values of “cm” or “inc”, dependingon whether ClientHeight is given in cm or inches, respectively, andClientScale is chosen between values of “kg” or “lbs” depending onwhether ClientWeight is given in kilograms or pounds, respectively.

The target BMI for the client (ClientBMI) can then be calculated asfollows:

${ClientBMI} = \left\{ \begin{matrix}\frac{{ClientWeight} + {IncrWeight}}{\left( {{ClientHeight}/100} \right)^{2}} & \begin{matrix}{{{if}\mspace{14mu} {ClientScale}} = {kg}} \\{{{{and}\mspace{14mu} {ClientScaleHeight}} = {cm}};}\end{matrix} \\\frac{{ClientWeight} + {IncrWeight}}{\left( {2.54 \times {{ClientHeight}/100}} \right)^{2}} & \begin{matrix}{{{if}\mspace{14mu} {ClientScale}} = {kg}} \\{{{{and}\mspace{14mu} {ClientScaleHeight}} = {Inc}};}\end{matrix} \\\frac{{0.453592 \times {ClientWeight}} + {IncrWeight}}{\left( {{ClientHeight}/100} \right)^{2}} & \begin{matrix}{{{if}\mspace{14mu} {ClientScale}} = {lbs}} \\{{{{and}\mspace{14mu} {ClientScaleHeight}} = {cm}};}\end{matrix} \\\frac{{0.453592 \times {ClientWeight}} + {IncrWeight}}{\left( {2.54 \times {{ClientHeight}/100}} \right)^{2}} & \begin{matrix}{{{if}\mspace{14mu} {ClientScale}} = {lbs}} \\{{{{and}\mspace{14mu} {ClientScaleHeight}} = {Inc}};}\end{matrix}\end{matrix} \right.$

After which the curve function can be applied to the ClientBMI to obtainthe BMI subscore S_(bmi). The formula of the curve function is:

$S_{bmi} = \left\{ \begin{matrix}0 & {{{{if}\mspace{14mu} {IBMI}} = {{{YES}\mspace{14mu} {and}\mspace{14mu} {ClientBMI}} < 15}};} \\{f_{c}\left( {{ClientBMI};{SCB}} \right)} & {{{{if}\mspace{14mu} {IBMI}} = {{{YES}\mspace{14mu} {and}\mspace{14mu} 15} \leq {ClientBMI} \leq 34}};} \\0 & {{{{if}\mspace{14mu} {IBMI}} = {{{YES}\mspace{14mu} {and}\mspace{14mu} {ClientBMI}} > 34}};} \\{NULL} & {{{{if}\mspace{14mu} {IBMI}} = {NO}},}\end{matrix} \right.$

where SCB={(15,0), (18.5,90), (20,100), (23,100), (25,90), (30,50),(34,0)} and IBMI is the BMI contribution indicator. FIG. 7C shows agraphical representation of the BMI curve function.

If IBMI=YES, the formula of S_(bmi(AG)) is given by:

$S_{{bmi}{({AG})}} = \left\{ \begin{matrix}78 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\69 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\65 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\69 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\87 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\70 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\60 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\63 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\68 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\69 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\61 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\56 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\72 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\71 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}},}\end{matrix} \right.$

Otherwise, S_(bmi(AG))=NULL. The above example data are average scoresof a Canadian population in various age and gender brackets.

The BMI health subscore S_(BMI) can be compared with the average scoreS_(BMI(AG)) of the general population in the client's age and gendercategory to determine whether the client's subscore is better than,equal to, or worse than others in the same age and gender category. Aqualitative scale can be used to indicate whether the client's BMIsubscore S_(bmi) is excellent, very good, good, fair, or poor. In someembodiments, the subscores for each rating may be provided in a rangesuch as, for example: Excellent: 86-100; Very good: 74-85; Good: 62-73;Fair: 50-61; and Poor: 0-50. It should be appreciated that any otherscore may be utilized.

Weight

As another example, a weight health subscore S_(wei) may be generated toindicate the individual or group's wellness with respect to weight, asranked in comparison to general population distribution information. Theinputs may be age (Clientage), gender (ClientGender), height(ClientHeight—in cm or inches), ClientScaleHeight (indicating whetherheight is in cm or inches), weight (ClientWeight), ClientScale(indicating whether weight is in kg or lbs), the weight that clientintends to change in kg (IncrWeight), and the contribution indicatorIWei. IWei is “YES” if the BMI/weight indicator is “YES” and theweight/BMI selector is “weight”.

The weight subscore can be determined by first defining function f_(w)():

${f_{w}(x)} = \left\{ \begin{matrix}{\left( \frac{ClientHeight}{100} \right)^{2} \times x} & \begin{matrix}{{{if}\mspace{14mu} {ClientScale}} = {{kg}\mspace{14mu} {and}}} \\{{{ClientScaleHeight} = {cm}};}\end{matrix} \\{\left( \frac{2.54 \times {ClientHeight}}{100} \right)^{2} \times x} & \begin{matrix}{{{if}\mspace{11mu} {ClientScale}} = {{kg}\mspace{14mu} {and}}} \\{{{ClientScaleHeight} = {Inc}};}\end{matrix} \\{\left( \frac{{ClientHeight}^{2}}{100^{2} \times 0.453592} \right) \times x} & \begin{matrix}{{{if}\mspace{14mu} {ClientScale}} = {{lbs}\mspace{14mu} {and}}} \\{{{ClientScaleHeight} = {cm}};}\end{matrix} \\{\left( \frac{\left( {2.54/{ClientHeight}} \right)^{2}}{100^{2}/0.453592} \right) \times x} & \begin{matrix}{{{if}\mspace{11mu} {ClientScale}} = {{lbs}\mspace{14mu} {and}}} \\{{ClientScaleHeight} = {{Inc}.}}\end{matrix}\end{matrix} \right.$

and defining:

${NPClientWeight} = \begin{Bmatrix}{{{ClientWeight} + {{IncrWeight}\mspace{14mu} {if}\mspace{14mu} {ClientScale}}} = {kg}} \\{{{ClientWeight} + {\frac{IncrWeight}{0.453592}\mspace{14mu} {if}\mspace{14mu} {ClientScale}}} = {lbs}}\end{Bmatrix}$

Then, the weight subscore S_(wei) is given by:

$S_{wei} = \left\{ \begin{matrix}{NULL} & {{{{if}\mspace{14mu} {IWei}} = {NO}};} \\0 & {{{{if}\mspace{14mu} {NPClientWeight}} < {f_{w}(15)}};} \\{f_{c}\left( {{NPClientWeight};{SCW}} \right)} & {{{{if}\mspace{14mu} {f_{w}(15)}} \leq {NPClientWeight} \leq {f_{w}(34)}};} \\0 & {{{{if}\mspace{14mu} {NPClientWeight}} > {f_{w}(34)}};}\end{matrix} \right.$

where SCW={(f_(w)(15),0), (f_(w)(18.5),90), (f_(w)(20),100),(f_(w)(23),100, (f_(w)(25),90), (f_(w)(30), 50), (f_(w)(34), 0)}

If IWei=YES, the calculation of S_(Wei(AG)) is the same as S_(bmi(AG)):

$S_{{wei}{({AG})}} = \left\{ \begin{matrix}78 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\69 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\66 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\65 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\69 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\87 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\70 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\60 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\63 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\68 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\69 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\61 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\56 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\72 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\67 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\71 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}},}\end{matrix} \right.$

If IWei=NO, S_(Wei(AG))=NULL. The above example data are average scoresof a Canadian population in various age and gender brackets.

The client's weight subscore S_(wei) can be compared with the averagescore S_(wei(AG)) of the general population in the client's age andgender category to determine whether the client's subscore is betterthan, equal to, or worse than others in the same age and gendercategory.

Waist Circumference

As another example, a waist circumference health subscore S_(wst) can begenerated to indicate the individual or group's wellness with respect towaist circumference, as ranked in comparison to waist circumference inthe general population distribution information. The inputs may be age(Clientage), gender (ClienGender), current waist circumference(ClientWaist0), length of waist in cm that client intends to change(IncrWaist), with negative values meaning a decrease in waistcircumference, and a waist contribution indicator (D598), which is ayes/no value that determines whether the waist subscore contributes tothe calculation of the client's overall wellness score. Additionally,ClientScale Waist is chosen between the values of “cm” or “inc”depending on whether ClientWaist0 is given in cm or inches,respectively.

A distribution of data of waist circumferences is provided, and can begrouped into the same age and gender brackets as above.

To obtain the client's waist subscore S_(wst), the client's target waistcircumference is first determined, for example by the following formula:

${ClientWaist} = \left\{ \begin{matrix}{\max \left( {0,{{2.5 \times {ClientWaist}\; 0} + {IncrWaist}}} \right)} & {{{if}\mspace{14mu} {ClientScaleWaist}} = {Inc}} \\{\max \left( {0,{{{ClientWaist}\; 0} + {IncrWaist}}} \right)} & {{{if}\mspace{14mu} {ClientScaleWaist}} = {cm}}\end{matrix} \right.$

The client's rank among the general population WaistRank can then becalculated based on ClientWaist and the distribution data of waistcircumference, as was done to calculate StepsRank. The waistdistribution data can be divided into eleven deciles as follows:

Cumulative percent (%) 0 10 20 30 40 50 60 70 80 90 100 Waist quartilew₀ w₁ w₂ w₃ w₄ w₅ w₆ w₇ w₈ w₉ w₁₀where w₀=w₁−[Σ_(i=1) ⁸(w_(i+1)−w_(i))]/8, w₁₀=w₉+[Σ_(i=1)⁸(w_(i+1)−w_(i))]/8, which differs from the deciles for steps and MVactivity time.

The rank of the client's waist relative to the general population canthen be estimated by the formula:

${WaistRank} = \left\{ {\begin{matrix}0 & {{{if}\mspace{14mu} {ClientWaist}} < w_{0}} \\{f_{c}\left( {{ClientWaist};{RW}} \right)} & {{{if}\mspace{14mu} w_{0}} \leq {ClientWaist} \leq w_{10}} \\100 & {{{if}\mspace{14mu} {ClientWaist}} > w_{10}}\end{matrix},} \right.$

where RW={(w₀,0), (w₁,10), (w₂,20), (w₃,30), . . . , (w₉,90), (w₉,100)}.After obtaining WaistRank, the curve function can be applied to obtainthe waist subscore S_(wst). The client's waist subscore S_(wst) can becompared with the average score S_(wst(AG)) of the general population inthe client's age and gender category to determine whether the client'ssubscore is better than, equal to, or worse than others in the same ageand gender category. A qualitative scale can be used to indicate whetherthe waist circumference health subscore S_(wst) is excellent, very good,good, fair, or poor. In some embodiments, the subscores for each ratingmay be provided in a range such as, for example: Excellent: 86-100; Verygood: 74-85; Good: 62-73; Fair: 50-61; and Poor: 0-50. It should beappreciated that any other score may be utilized.

Smoking

As another example, a smoking health subscore S_(smk) can be generatedto indicate the individual or group's wellness with respect to smokinghabits, based upon how the individual or group ranks compared to thegeneral population information. The inputs for the smoking subscore mayinclude age (Clientage), gender (ClientGender), a smoking contributionindicator (D599), which is a yes/no value that determines whether thedrinking subscore contributes to the calculation of the client's overallwellness score, as well as the variable shown in FIG. 8. Additionally,distribution data of the general population can be obtained for each ofthe following smoking levels for various age brackets and genders: NeverSmoked, Former Occasional Smoker, Former Daily Smoker, Always anOccasional Smoker, Occasional Smoker and Former Daily Smoker, and DailySmoker.

The smoking subscore is given by the following formula:

$S_{smk} = \left\{ \begin{matrix}100 & {{{if}\mspace{14mu} {ClientSmoke}} = Y} \\{85 + {\left( {{{ClientSmokeF}\; {OYear}} - 1} \right) \times \frac{100 - 85}{9 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientSmoke}} = {N\mspace{14mu} {and}}} \\{{ClientSmokeFO} = Y}\end{matrix} \\{70 + {\left( {{ClientSmokeFDNu} - 1} \right) \times \frac{100 - 70}{14 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientSmoke}} = {N\mspace{14mu} {and}}} \\{{ClientSmokeFD} = Y}\end{matrix} \\{70 - {\left( {{ClientSmokeAODa} - 1} \right) \times \frac{70 - 0}{29 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientSmoke}} = {N\mspace{14mu} {and}}} \\{{ClientSmokeAO} = Y}\end{matrix} \\{60 + {\left( {{ClientSmokeOSNu} - 1} \right) \times \frac{60 - 0}{29 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientSmoke}} = {N\mspace{14mu} {and}}} \\{{ClientSmokeOS} = Y}\end{matrix} \\{20 + {\left( {{CigarNumber} - 1} \right) \times \frac{20 - 0}{39 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientSmoke}} = {N\mspace{14mu} {and}}} \\{{ClientSmokeD} = Y}\end{matrix}\end{matrix} \right.$

If smoking contribution indicator D599=NO, then S_(smk)=NULL.

If D599=YES, the formula of S_(smk(AG)) is given by:

$S_{{wei}{({AG})}} = \left\{ \begin{matrix}\begin{matrix}{{100 \times C\; 2250} + {92 \times C\; 2251} + {85 \times C\; 2252}} \\{{{+ 50} \times C\; 2253} + {43 \times C\; 2254} + {12 \times C\; 2255}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = M} \\{{{{and}\mspace{14mu} 20} \leq {Clientage} < 30};}\end{matrix} \\\begin{matrix}{{100 \times D\; 2250} + {92 \times D\; 2251} + {85 \times D\; 2252}} \\{{{+ 50} \times D\; 2253} + {43 \times D\; 2254} + {12 \times D\; 2255}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = F} \\{{{{and}\mspace{14mu} 20} \leq {Clientage} < 30};}\end{matrix} \\\begin{matrix}{{100 \times E\; 2250} + {92 \times E\; 2251} + {85 \times E\; 2252}} \\{{{+ 50} \times E\; 2253} + {43 \times E\; 2254} + {12 \times E\; 2255}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = M} \\{{{{and}\mspace{14mu} 30} \leq {Clientage} < 40};}\end{matrix} \\{{100 \times F\; 2250} + {92 \times F\; 2251} + {85 \times F\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = F} \\{{{+ 50} \times F\; 2253} + {43 \times F\; 2254} + {12 \times F\; 2255}} & {{{{and}\mspace{14mu} 30} \leq {Clientage} < 40};} \\{{100 \times G\; 2250} + {92 \times G\; 2251} + {85 \times G\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = M} \\{{{+ 50} \times G\; 2253} + {43 \times G\; 2254} + {12 \times G\; 2255}} & {{{{and}\mspace{14mu} 40} \leq {Clientage} < 50};} \\{{100 \times H\; 2250} + {92 \times H\; 2251} + {85 \times H\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = F} \\{{{+ 50} \times H\; 2253} + {43 \times H\; 2254} + {12 \times H\; 2255}} & {{{{and}\mspace{14mu} 40} \leq {Clientage} < 50};} \\{{100 \times I\; 2250} + {92 \times I\; 2251} + {85 \times I\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = M} \\{{{+ 50} \times I\; 2253} + {43 \times I\; 2254} + {12 \times I\; 2255}} & {{{{and}\mspace{14mu} 50} \leq {Clientage} < 60};} \\{{100 \times J\; 2250} + {92 \times J\; 2251} + {85 \times J\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = F} \\{{{+ 50} \times J\; 2253} + {43 \times J\; 2254} + {12 \times J\; 2255}} & {{{{and}\mspace{14mu} 50} \leq {Clientage} < 60};} \\{{100 \times K\; 2250} + {92 \times K\; 2251} + {85 \times K\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = M} \\{{{+ 50} \times K\; 2253} + {43 \times K\; 2254} + {12 \times K\; 2255}} & {{{{and}\mspace{14mu} {Clientage}} \geq 60};} \\{{100 \times L\; 2250} + {92 \times L\; 2251} + {85 \times L\; 2252}} & {{{if}\mspace{14mu} {ClientGender}} = F} \\{{{+ 50} \times L\; 2253} + {43 \times L\; 2254} + {12 \times L\; 2255}} & {{{{and}\mspace{14mu} {Clientage}} \geq 60},}\end{matrix} \right.$

where C-L combined with numbers 2250-2257 are references to a generalpopulation distribution information database with respect to the smokinglevels of the general population. Examples of general populationinformation can be found in FIG. 9.

If D599=NO, then S_(smk(AG))=NULL. Accordingly, the smoking healthsubscore S_(smk) can be compared with the score S_(smk(AG)) of thegeneral population in the group corresponding in age and gender todetermine whether the health subscore is better than, equal to, or worsethan others in the same age and gender category. A qualitative scale canbe used to indicate whether the client's smoking subscore S_(smk) isexcellent, very good, good, fair, or poor. In some embodiments, thesubscores for each rating may be provided in a range such as, forexample: Excellent: 86-100; Very good: 74-85; Good: 62-73; Fair: 50-61;and Poor: 0-50. It should be appreciated that any other score may beutilized.

Drinking

As another example, a drinking Health Subscore S_(drk) can be generatedto the individual or group's wellness with respect to drinking habits,based upon how the individual or group ranks compared with the generalpopulation information. The drinking Health Subscore may be generatedusing age (Clientage), gender (ClientGender), a drinking contributionindicator (D600), which is ayes/no value that determines whether thedrinking subscore contributes to the calculation of the client's overallwellness score, as well as the factors shown in FIG. 10. Additionally,distribution data of the general population is provided for each of thefollowing drinking levels for various age brackets and genders: RegularDrinker, Occasional Drinker, Former Drinker, Never Drink.

The drinking subscore S_(drk) is given by the following formula:

$S_{drk} = \left\{ \begin{matrix}100 & {{{if}\mspace{14mu} {ClientDrkND}} = Y} \\{85 + {\left( {{ClientDrkFDNu} - 1} \right) \times \frac{100 - 85}{4 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = {N\mspace{14mu} {and}}} \\{{ClientDrkFD} = Y}\end{matrix} \\{95 - {\left( {{ClientDrkODNu} - 1} \right) \times \frac{95 - 50}{14 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = {N\mspace{14mu} {and}}} \\{{ClientDrkOD} = {Y\mspace{14mu} {and}}} \\{{ClientGender}\mspace{14mu} {is}\mspace{14mu} {NOT}\; F}\end{matrix} \\{50 - {\left( {{ClientDrkODNu} - 8} \right) \times \frac{50 - 0}{21 - 8}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = {N\mspace{14mu} {and}}} \\{{ClientDrkOD} = {Y\mspace{14mu} {and}}} \\{{ClientGender} = {F\mspace{14mu} {and}}} \\{{ClientDrkODNu} > 7}\end{matrix} \\{90 - {\left( {{ClientDrkODNu} - 1} \right) \times \frac{90 - 50}{7 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = {N\mspace{14mu} {and}}} \\{{ClientDrkOD} = {Y\mspace{14mu} {and}}} \\{{ClientGender} = {F\mspace{14mu} {and}}} \\{{ClientDrkODNu}<=7}\end{matrix} \\{95 - {\left( {{ClientDrkRDNu} - 1} \right) \times \frac{95 - 50}{14 - 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = {N\mspace{14mu} {and}}} \\{{ClientDrkRD} = {Y\mspace{14mu} {and}}} \\{{ClientGender}\mspace{14mu} {is}\mspace{14mu} {NOT}\; F\mspace{14mu} {and}} \\{{ClientDrkODNu} < 15}\end{matrix} \\{50 - {\left( {{ClientDrkRDNu} - 15} \right) \times \frac{50 - 0}{28 - 15}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = {N\mspace{14mu} {and}}} \\{{ClientDrkRD} = {Y\mspace{14mu} {and}}} \\{{ClientGender}\mspace{14mu} {is}\mspace{14mu} {NOT}\; F\mspace{14mu} {and}} \\{{ClientDrkRDNu}>=15}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = N} \\{{ClientDrkRD} = {Y\mspace{14mu} {and}}} \\{{ClientGender} = {F\mspace{14mu} {and}}} \\{{ClientDrkRDNu} > 14}\end{matrix} \\{50 - {\left( {{ClientDrkRDNu} - 8} \right) \times \frac{50 - 0}{21 - 8}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientDrkND}} = N} \\{{ClientDrkRD} = {Y\mspace{14mu} {and}}} \\{{ClientGender} = {F\mspace{14mu} {and}}} \\{{ClientDrkRDNu}<=14}\end{matrix}\end{matrix} \right.$

If the drinking contribution indicator D600=NO, then S_(drk)=NULL.

If D600=YES, the formula of S_(drk(AG)) is given by:

$S_{{drk}{({AG})}} = \left\{ \begin{matrix}\begin{matrix}{{69 \times C\; 2228} + {95 \times C\; 2229} +} \\{{93 \times C\; 2230} + {100 \times C\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {M\mspace{14mu} {and}}} \\{{20 \leq {Clientage} < 30};}\end{matrix} \\\begin{matrix}{{69 \times E\; 2228} + {95 \times E\; 2229} +} \\{{93 \times E\; 2230} + {100 \times E\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {M\mspace{14mu} {and}}} \\{{30 \leq {Clientage} < 40};}\end{matrix} \\\begin{matrix}{{69 \times G\; 2228} + {95 \times G\; 2229} +} \\{{93 \times G\; 2230} + {100 \times G\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {M\mspace{14mu} {and}}} \\{{40 \leq {Clientage} < 50};}\end{matrix} \\\begin{matrix}{{69 \times I\; 2228} + {95 \times I\; 2229} +} \\{{93 \times I\; 2230} + {100 \times I\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {M\mspace{14mu} {and}}} \\{{50 \leq {Clientage} < 60};}\end{matrix} \\\begin{matrix}{{69 \times K\; 2228} + {95 \times K\; 2229} +} \\{{93 \times K\; 2230} + {100 \times K\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {M\mspace{14mu} {and}}} \\{{\leq {Clientage} \geq 60};}\end{matrix} \\\begin{matrix}{{69 \times D\; 2228} + {95 \times D\; 2229} +} \\{{93 \times D\; 2230} + {100 \times D\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {F\mspace{14mu} {and}}} \\{{20 \leq {Clientage} < 30};}\end{matrix} \\\begin{matrix}{{69 \times F\; 2228} + {95 \times F\; 2229} +} \\{{93 \times F\; 2230} + {100 \times F\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {F\mspace{14mu} {and}}} \\{{30 \leq {Clientage} < 40};}\end{matrix} \\\begin{matrix}{{69 \times H\; 2228} + {95 \times H\; 2229} +} \\{{93 \times H\; 2230} + {100 \times H\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {F\mspace{14mu} {and}}} \\{{40 \leq {Clientage} < 50};}\end{matrix} \\\begin{matrix}{{69 \times J\; 2228} + {95 \times J\; 2229} +} \\{{93 \times J\; 2230} + {100 \times J\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {F\mspace{14mu} {and}}} \\{{50 \leq {Clientage} < 60};}\end{matrix} \\\begin{matrix}{{69 \times L\; 2228} + {95 \times L\; 2229} +} \\{{93 \times L\; 2230} + {100 \times L\; 2231}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientGender}} = {F\mspace{14mu} {and}}} \\{{\leq {Clientage} \geq 60},}\end{matrix}\end{matrix} \right.$

where C-L combined with numbers 2250-2257 are references to a generalpopulation distribution information database with respect to thedrinking levels of the general population.

If D600=NO, then S_(drk(AG))=NULL.

The client's drinking subscore S_(drk) can be compared with the scoreS_(drk(AG)) of the general population in the client's age and gendercategory to determine whether the client's subscore is better than,equal to, or worse than others in the same age and gender category. Aqualitative scale can be used to indicate whether the client's drinkingsubscore S_(drk) is excellent, very good, good, fair, or poor. In someembodiments, the subscores for each rating may be provided in a rangesuch as, for example: Excellent: 86-100; Very good: 74-85; Good: 62-73;Fair: 50-61; and Poor: 0-50. It should be appreciated that any otherscore may be utilized.

Resting Estimated VO2 Max

VO2 Max is an individual's maximal oxygen consumption and can bemeasured in a variety of ways. Accordingly, as another example, thereare a number of VO2 Max Health Subscores that can be generated and usedin the calculation of the overall wellness score (as described in moredetail below).

In some embodiments, the VO2 Max subscore S_(vr) may be based upon anestimation of the VO2max based on resting heart rate. The inputs forgenerating the VO2 max subscore based on resting heart rate can beclient's age (Clientage), gender (ClientGender), resting heart rate(HR20Second), which may be taken over a predetermined period of timesuch as, for example, over an interval or seconds to minutes, orpreferably over a period of 20 seconds, and a resting estimated VO2 maxcontribution indicator (D601), which is a yes/no value that determineswhether the resting VO2 max subscore contributes to the calculation ofthe overall wellness score. Additionally, general populationdistribution data of the resting heart rate and VO2 max norms of thegeneral population is provided for various age brackets and genders,which can be tabulated as shown in FIG. 11. The population distributiondata of resting heart rate and VO2 max norms can be tabulated in adatabase (see, for example, FIG. 12). As would be understood, thepresent database may comprise a software database operative for fast andconvenient access. In some embodiments, population data for Canada isprovided. Having regard to FIG. 12, seven columns are provided asrepresentation of seven possible levels of VO2max: Low, Fair, Average,Good, High, Athletic, Olympic. The upper six rows are six age levels ofthe female group (12-19, 20-29, 30-39, 40-49 50-65, and 65+). The lowerseven rows are seven age levels of the male group: 12-19, 20-29, 30-39,40 49, 50-59, 60-69, and 70+. HR20Second, which represents the client'sresting heart rate, can be obtained by the client's device or manuallyentered by the client.

The resting estimated VO2max subscore S_(vr) can be estimated by thefollowing formula:

$S_{vr} = {{F\left( {{VO}\; 2\; {Resting}} \right)} = \left\{ \begin{matrix}{f\left( {{{VO}\; 2\; {Resting}},{B\; 1854},{H\; 1854}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 20} \leq {Clientage} < {30\mspace{14mu} {and}}} \\{{{ClientGender} = F};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1855},{H\; 1855}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 30} \leq {Clientage} < {40\mspace{14mu} {and}}} \\{{{ClientGender} = F};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1856},{H\; 1856}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 40} \leq {Clientage} < {50\mspace{14mu} {and}}} \\{{{ClientGender} = F};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1857},{H\; 1857}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 50} \leq {Clientage} < {65\mspace{14mu} {and}}} \\{{{ClientGender} = F};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1858},{H\; 1858}} \right)} & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} \geq {65\mspace{14mu} {and}}} \\{{{ClientGender} = F};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1854},{H\; 1854}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 20} \leq {Clientage} < {30\mspace{14mu} {and}}} \\{{{ClientGender} = M};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1855},{H\; 1855}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 30} \leq {Clientage} < {40\mspace{14mu} {and}}} \\{{{ClientGender} = M};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1856},{H\; 1856}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 40} \leq {Clientage} < {50\mspace{14mu} {and}}} \\{{{ClientGender} = M};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1857},{H\; 1857}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 50} \leq {Clientage} < {60\mspace{14mu} {and}}} \\{{{ClientGender} = M};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1858},{H\; 1858}} \right)} & \begin{matrix}{{{if}\mspace{14mu} 60} \leq {Clientage} < {70\mspace{14mu} {and}}} \\{{{ClientGender} = M};}\end{matrix} \\{f\left( {{{VO}\; 2\; {Resting}},{B\; 1859},{H\; 1859}} \right)} & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} \geq {70\mspace{14mu} {and}}} \\{{{ClientGender} = M},}\end{matrix}\end{matrix} \right.}$

where F( ) denotes S_(vr) as a function of VO2 Resting, B&H combinedwith numbers 1854-1859refer to the cells of the population distributiontable for VO2 Max shown in FIG. 12, and the VO2Resting is calculated by:

${{VO}\; 2\; {Resting}} = {5.1 \times \frac{220 - {Clientage}}{{HR}\; 20\; {Second}}}$

The function f( , , , ) can then be defined by the following formula:

${f\left( {x,a,b} \right)} = \left\{ \begin{matrix}\frac{\left( {x - 0} \right) \times \left( {{{SC}\; 50\; P} - {B\; 0}} \right)}{a - 6} & {{{{if}\mspace{14mu} x} < a};} \\{{{SC}\; 50\; P} + \frac{\left( {x - a} \right) \times \left( {{{SC}\; 100\; P} - {{SC}\; 50\; P}} \right)}{b - a}} & {{{{if}\mspace{14mu} a} \leq x < b};} \\100 & {{{if}\mspace{14mu} x} \geq {b.}}\end{matrix} \right.$

where SC100P, SC50P, and S0 are 100, 40, and 0, respectively.

If the resting estimated VO2 max contribution indicator D601=NO, thenS_(vr(AG))=NULL. If D601=YES, S_(vr(AG)) is given by:

  S_(vr(AG)) = F(VO 2 RestingAgeGen)   where$\mspace{20mu} {{{VO}\; 2\; {RestingAgeGen}} = {5.1 \times \frac{220 - {Clientage}}{{HR}\; 20\; {SecondAGen}}}}$  and ${{VO}\; 2\; {RestingAgeGen}} = \left\{ \begin{matrix}74.4 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 15}};} \\70 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 15} \leq {Clientage} < 20}};} \\69.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 25}};} \\71.1 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 25} \leq {Clientage} < 30}};} \\68.8 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 35}};} \\69.6 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 35} \leq {Clientage} < 40}};} \\68.2 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 45}};} \\69.6 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 45} \leq {Clientage} < 50}};} \\67.5 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\66.4 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\65.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\78.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 15}};} \\76.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 15} \leq {Clientage} < 20}};} \\76.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 25}};} \\76.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 25} \leq {Clientage} < 30}};} \\75.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 35}};} \\73.1 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 35} \leq {Clientage} < 40}};} \\71.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 45}};} \\72.3 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 45} \leq {Clientage} < 50}};} \\69.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 65}};} \\68.2 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 65.}}\end{matrix} \right.$

Treadmill Test Estimated VO2 Max

In some other embodiments, a VO2 Max health subscore S_(vt) may begenerated to indicate the individual or group's wellness with respect toan estimation of the client's VO2 Max based on the client's heart ratesat the end of two stages of exercise: stage 1 and stage 2, where stage 1and stage 2 represent two different intensities of exercise, and wherestage 2 is more intense than stage 1. Both stages may be customized foreach individual based on their age, gender and resting heart rate. Theinputs for generating the treadmill test estimate VO2max subscore may beage (Clientage), gender (ClientGender), the heart rates at the end ofstage 1 and stage 2 exercise, and a treadmill test estimated VO2maxcontribution indicator (IVO2T), which is a yes/no value that determineswhether the treadmill VO2 max subscore S_(vt) contributes to thecalculation of the client's overall wellness score. Additionally,population data of VO2 max norms, predicted VO2max in stage 1 and stage2 exercise, and population data of estimated VO2 max are also used. Thisdata can be tabulated as in FIG. 11 showing VO2 Max of GeneralPopulation.

Contribution indicator IVO2T is determined by:

${{IVO}\; 2\; T} = \left\{ \begin{matrix}{YES} & {\mspace{14mu} \begin{matrix}{{{if}\mspace{14mu} D\; 602} = {{{Yes}\mspace{14mu} {and}\mspace{14mu} B\; 602} = {{Estim}.}}} \\{{{VO}\; 2\mspace{14mu} {Max}\mspace{14mu} ({Treadmill})};}\end{matrix}} \\{NO} & {{otherwise},}\end{matrix} \right.$

where D602 is the indicator of whether any one of the treadmill testestimated VO2max and model based estimated VO2max is taken into thecalculation of overall score, and B602 is the indicator of which one ofthe two VO2max is chosen.

A population data of predicted VO2max of stage 1 and stage 2 can betabulated in a database for fast and convenient access (e.g. such as anExcel™ spreadsheet). In one embodiment, general population distributiondata for Canada may be obtained from any appropriate sources including,for example, the “National Youth Fitness Survey Treadmill ExaminationManual”, Appendix C, and tabulated as provided in FIG. 13. The client'sheart rates at the end of stage 1 and stage 2 exercise are denoted asHRs1Tread and HRs12Tread, respectively. The Canadian population data ofestimated VO2 max can be tabulated as in FIG. 11.

The variables VO2 Tread and VO2 TreadAgeGen are used to calculate thetreadmill estimated VO2 max subscore S_(vt). The following informationis required to calculated VO2 Tread:

${x\; 1\mspace{14mu} \left( {{submax}\mspace{14mu} {VO}\; 2\mspace{14mu} {at}\mspace{14mu} {end}\mspace{14mu} {of}\mspace{14mu} {Stage}\mspace{14mu} 1} \right)} = \left\{ {{\begin{matrix}{H\; 1677} & {{{{if}\mspace{14mu} {PredVO}\; 2\; \max} < 20};} \\{H\; 1678} & {{{{if}\mspace{14mu} 20} \leq {{PredVO}\; 2\; \max} < 25};} \\{H\; 1679} & {{{{if}\mspace{14mu} 25} \leq {{PredVO}\; 2\; \max} < 30};} \\{H\; 1680} & {{{{if}\mspace{14mu} 30} \leq {{PredVO}\; 2\; \max} < 35};} \\{H\; 1681} & {{{{if}\mspace{14mu} 35} \leq {{PredVO}\; 2\; \max} < 40};} \\{H\; 1682} & {{{{if}\mspace{14mu} 40} \leq {{PredVO}\; 2\; \max} < 45};} \\{H\; 1683} & {{{{if}\mspace{14mu} 45} \leq {{PredVO}\; 2\; \max} < 50};} \\{H\; 1684} & {{{if}\mspace{14mu} {PredVO}\; 2\; \max} \geq 50.}\end{matrix}x\; 2\mspace{14mu} \left( {{submax}\mspace{14mu} {Vo}\; 2\mspace{14mu} {at}\mspace{14mu} {end}\mspace{14mu} {of}\mspace{14mu} {Stage}\mspace{14mu} 2} \right)} = \left\{ \begin{matrix}{K\; 1677} & {{{{if}\mspace{14mu} {PredVO}\; 2\; \max} < 20};} \\{K\; 1678} & {{{{if}\mspace{14mu} 20} \leq {{PredVO}\; 2\; \max} < 25};} \\{K\; 1679} & {{{{if}\mspace{14mu} 25} \leq {{PredVO}\; 2\; \max} < 30};} \\{K\; 1680} & {{{{if}\mspace{14mu} 30} \leq {{PredVO}\; 2\; \max} < 35};} \\{K\; 1681} & {{{{if}\mspace{14mu} 35} \leq {{PredVO}\; 2\; \max} < 40};} \\{K\; 1682} & {{{{if}\mspace{14mu} 40} \leq {{PredVO}\; 2\; \max} < 45};} \\{K\; 1683} & {{{{if}\mspace{14mu} 45} \leq {{PredVO}\; 2\; \max} < 50};} \\{K\; 1684} & {{{if}\mspace{14mu} {PredVO}\; 2\; \max} \geq 50.}\end{matrix} \right.} \right.$

VO2 Tread is given by:

${{VO}\; 2\; {Tread}} = {{\left( {220 - {Clientage} - \frac{{{HRs}\; 1\; {Tread}} + {{HRs}\; 2\; {Tread}}}{2}} \right) \times \left( \frac{{x\; 2} - {x\; 1}}{{{HRs}\; 2\; {Tread}} - {{HRs}\; 1\; {Tread}}} \right)} + \frac{{x\; 2} + {x\; 1}}{2}}$

VO2TreadAgeGen is given by:

${{VO}\; 2\; {TreadAgeGen}} = \left\{ \begin{matrix}45 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 15}};} \\46.1 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 15} \leq {Clientage} < 20}};} \\45.1 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 25}};} \\43 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 25} \leq {Clientage} < 30}};} \\42.1 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 35}};} \\40.8 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 35} \leq {Clientage} < 40}};} \\40.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 45}};} \\40 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 45} \leq {Clientage} < 50}};} \\38.3 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\36.4 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\35.5 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\65.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 15}};} \\76.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 15} \leq {Clientage} < 20}};} \\76.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 25}};} \\76.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 25} \leq {Clientage} < 30}};} \\75.9 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 35}};} \\73.1 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 35} \leq {Clientage} < 40}};} \\71.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 45}};} \\72.3 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 45} \leq {Clientage} < 50}};} \\69.7 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 65}};} \\68.2 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 65.}}\end{matrix} \right.$

The data above being the estimated VO2 max for various age brackets andgenders found in column K, rows 1732-1753 of FIG. 11, above.

The treadmill estimated VO2 max subscore is given by:

$S_{vt} = \left\{ \begin{matrix}{F\left( {{VO}\; 2\; {Tread}} \right)} & {{{{if}\mspace{14mu} {IVO}\; 2\; T} = {YES}};} \\{NULL} & {{{if}\mspace{14mu} {IVO}\; 2\; T} = {{NO}.}}\end{matrix} \right.$

where the function F( ) is the function used above for calculating thesubscore of resting estimated VO2 max.

The formula of S_(vt(AG)) is given by:

$S_{{vt}{({AG})}} = \left\{ \begin{matrix}{F\left( {{VO}\; 2\; {TreadAgeGen}} \right)} & {{{{if}\mspace{14mu} {IVO}\; 2\; T} = {YES}};} \\{NULL} & {{{if}\mspace{14mu} {IVO}\; 2\; T} = {{NO}.}}\end{matrix} \right.$

Model Estimated VO2 Max

In yet other embodiments, a VO2 Max Health Subscore S_(vm) may generatedto indicate the individual or group's wellness with respect to anestimation of the client's VO2 Max based on the age (Clientage), gender(ClientGender), BMI (ClientBMI), Physical Activity Rate (PARScore), theclient's resting heart rate (HR20Second), and the model estimated VO2max contribution indicator IVO2M, which is a yes/no value thatdetermines whether the model estimated VO2 max subscore S_(vm)contributes to the calculation of the client's overall wellness score.

As above, population data and the individual's data (if available) ofheart rate and perceived exertion rating of 3 stages of exercise mayalso used (warm-up, stage 1, stage 2) to calculate the subscore, as wellas population estimates of parameters in the linear model of estimatingVO2 max, and population data of estimated VO2 max.

Contribution indicator IVO2M is determined by:

${{IVO}\; 2\; M} = \left\{ \begin{matrix}{YES} & {\mspace{14mu} \begin{matrix}{{{if}\mspace{14mu} D\; 602} = {{{Yes}\mspace{14mu} {and}\mspace{14mu} B\; 602} = {{Estim}.}}} \\{{{VO}\; 2\mspace{14mu} {Max}\mspace{14mu} ({Model})};}\end{matrix}} \\{NO} & {{otherwise},}\end{matrix} \right.$

where D602, as described in the treadmill estimated VO2 max sectionabove, is the indicator of whether any one of the treadmill testestimated VO2 max and model based estimated VO2 max is taken into thecalculation of the overall score, and B602 is the indicator of which oneof the two VO2 max is chosen.

A physical activity rate PARscore may be obtained through, for example,questions similar to the following: Would you say that you avoid walkingor exertion? PARScore=0; You walk for pleasure and routinely use stairs?PARScore =1; You participate in regularly modest physical activity for:10 to 60 minutes per week? PARScore=2; More than 60 minutes per week?PARScore=3; You participate regularly in heavy physical activity for:Less than 30 minutes per week? PARScore=4; 30 to 60 minutes per week?PARScore=5; 1 to 3 hours per week? PARScore=6; More than 3 hours perweek? PARScore =7.

If the incoming wellness information show heart rates of the 3-stageexercise test (warm-up, stage 1, stage 2) and perceived exertion ratingof the 2-stage exercise test(stage 1, stage 2), then such informationmay also be taken into account. Otherwise, such incoming wellnessinformation is estimated based on the population data of heart rate andperceived exertion rating.

In some embodiments, the population data is located at columns D-I androws 1732-1753 of the VO2 Max of General Population shown in FIG. 11.The columns D, F, H represent heart rates (per minute) in the threestages of exercise. Columns E, G, I represent the rating of perceivedexertion in the three stages. The upper 11 rows (1732-1742) are 11 agegroups of male: 12-14, 15-19, 20-24, 25-29, 30- 34, 35-39, 40-44, 45-49,50-59, 60-69, and 70+. The lower 10 rows are 10 age groups of female:12-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-65, and 65+.A linear model can be used to estimate the client's VO2 max. Theestimates of parameters among the population can be tabulated in adatabase for convenient manipulation and tabulated as shown in FIG. 14,wherein data cell C1821 contains the intercept estimate. CellsC1822-1829 are coefficient estimates associated with age, resting heartrate, warm up heart rate, stage 1 heart rage, stage 2 heart rate,predicted VO2 max, stage 1 perceived rating, and stage 2 perceivedrating.

General population data of estimated VO2 max can also be tabulated, asshown in FIG. 11, at column K, rows 1732-1753. The client's heart ratein the 3 stages of exercise (warm up, stage 1, stage 2), denoted as HRw,HR1, and HR2, respectively, and perceived exertion rating in the 2stages (stage 1, stage 2), denoted as PR1 and PR2, respectively, isrequired in order to calculate VO2 Model1. If the client has providedactual values for HRw, HR1, HR2, PR1, and PR2, then those values can beused. Otherwise, these values can be estimated by referring to the VO2Max of General Population (FIG. 11). HRw values for various age bracketsand genders are found in column D, HR1 in column F, PR1 in column G, HR2in column H, and PR2 in column I. The VO2 Model1 can be calculated by:

VO2Modeli=C1821+C1824×HRw+C1825×HR1+C1828×PR1+C1826×HR2+C1829×PR2+C1827×PredVO2max+C1822×Clientage+3×C1823×HE20Second

where C1821-C1827 refers to the cells of FIG. 14. The VO2 ModellAgeGenis the same as VO2 TreadAgeGen. The model estimated VO2 max subscoreS_(vm) can then be calculated by:

$S_{vm} = \left\{ \begin{matrix}{F\left( {{VO}\; 2\; {Model}\; 1} \right)} & {{{{if}\mspace{14mu} {IVO}\; 2\; M} = {YES}};} \\{NULL} & {{{if}\mspace{14mu} {IVO}\; 2\; M} = {{NO}.}}\end{matrix} \right.$

where the function F( ) is the function used above for calculating thesubscore of resting and treadmill estimated VO2 max.

The formula of S_(vm(AG)) is given by:

$S_{{vm}{({AG})}} = \left\{ \begin{matrix}{F\left( {{VO}\; 2\; {Model}\; 1{AgeGen}} \right)} & {{{{if}\mspace{14mu} {IVO}\; 2\; M} = {YES}};} \\{NULL} & {{{if}\mspace{14mu} {IVO}\; 2\; M} = {{NO}.}}\end{matrix} \right.$

Disease Risk

Disease Risk digital biomarker subscores can be generated to determine(estimate or predict) an individual or group's risk of developingcertain diseases. In some embodiments, Disease Risk Health Subscores maybe calculated based on incoming wellness information such as, withoutlimitation, demographic information, Health Behaviours Subscores, familyhistory, and other factors, as compared to corresponding data from thegeneral population. In some embodiments, disease risk S_(DR) may, forexample, be generated by calculating an average of the subscoresgenerated for at least five different disease risk metrics including,without limitation, cardiovascular disease (S_(cardio)), diabetes(S_(diabet)), arthritis (S_(arthri)), lung disease (S_(lung)), and lowerback pain (S_(lbpain)). The disease risk subscore of the generalpopulation for any given age bracket and gender (S_(DR(AG))) is theaverage of S_(cardio(AG)), (S_(diabet(AG)), S_(arthri(AG)),S_(lung(AG)), and S_(lbpain(AG)). By way of example, embodiments showingmethods of generating a cardiovascular disease subscore S_(cardio) aredescribed, however it would be understood that similar methods may beused to determine health subscores for other disease risks.

Cardiovascular Disease

Accordingly, by way of example, a cardiovascular disease subscore can begenerated based upon, at least, some or all of the incoming wellnessinformation shown in FIG. 1. Additionally, general populationinformation relating to, at least, steps, MV activity time, BMI, andwaist can be used as a baseline with which to compare the individual orgroup. As would be known, normal blood pressure is typically defined asdiastolic <90 and systolic <140. Logistic models, requiring variousintercepts and coefficients used to predict the risk of cardiovasculardisease, can be used to calculate the individual's cardiovasculardisease risk CAvgRisk. A curve function can then be applied to CAvgRiskto obtain the cardiovascular disease subscore S_(cardio).

First, ClientBPR must be determined, which is a function of ClientBPRDisand BPRSitu:

${ClientBPR} = \left\{ \begin{matrix}Y & {{{if}\mspace{14mu} {ClientBPRDis}} = {{Y\mspace{14mu} {and}\mspace{14mu} {BPRt}} = N}} \\N & {{otherwise}.}\end{matrix} \right.$

Then, ClientCardio must also be determined:

${ClientCardio} = \left\{ \begin{matrix}Y & {{{if}\mspace{14mu} {ClientCarDis}} = {{Y\mspace{14mu} {and}\mspace{14mu} {CardioSitu}} =}} \\\; & {{{``{{Have}\mspace{14mu} {disease}\mspace{14mu} {but}\mspace{14mu} {medication}\mspace{14mu} {don}}’}t\mspace{14mu} {make}\mspace{14mu} {it}\mspace{14mu} {normal}}"} \\N & {{{if}\mspace{14mu} {ClientCarDis}} = {N\mspace{14mu} {or}\mspace{14mu} \left\{ {{ClientCarDis} = {{Y\mspace{14mu} {and}\mspace{14mu} {CardioSitu}} =}} \right.}} \\\; & \left. {``{{Have}\mspace{14mu} {disease}\mspace{14mu} {but}\mspace{14mu} {medication}\mspace{14mu} {makes}\mspace{14mu} {it}\mspace{14mu} {normal}}"} \right\} \\{FALSE} & {{{if}\mspace{14mu} {ClientCarDis}} = {{Y\mspace{14mu} {and}\mspace{14mu} {CardioSitu}} = {NA}}}\end{matrix} \right.$

The client's risk of cardiovascular disease CAvgRisk can be obtained by:

CAvgRisk=1/6(RCar1+RCar2+RCar3+RCar4+RCar5+RCar6)

where the RCar1, RCar2, RCar3, RCar4, Rcar5, Rcar6 are the riskscalculated from six models with the following six groups ofvariables/factors, respectively:

1. ClientStepAvgActi&newClientBMI&ClientCarFamily

2. ClientMVAvgActi&ClientBMI&ClientCarFamily

3. ClientStepAvgActi&ClientBPR&ClientGender

4. newClientBMI&ClientBPR&ClientGender

5. ClientMVAvgActi&ClientBPR&ClientGender

6. ClientWaist&ClientGender,

The following are the formulae to calculate risk using the above models.

1. The risk RCar1 estimated based on ClientStepAvgActi, newClientBMI,ClientCarFamily is:

$\begin{matrix}{{{RCar}\; 1} = {{CSBF}\left( {{ClientStepAvgActi},{newClientBMI},{ClientCarFamily}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{Y\; 1} \right)} & {{{{if}\mspace{14mu} {ClientCarFamily}} = Y};} \\{{logistic}\left( g_{N\; 1} \right)} & {{{{if}\mspace{14mu} {ClientCarFamily}} = N},}\end{matrix} \right.}\end{matrix}$

where CSBF( , , ) denotes RCar1 as a function of ClientStepAvgActi,newClientBMI, ClientCarFamily, the logistic( ) is the logistic function:

${{{logistic}(x)} = \frac{\exp (x)}{1 + {\exp (x)}}},$

and

g_(Y1)=SBF1CIntO+SBF1CStO×ClientStepAvgActi+SBF1CBmO×newClientBMI+SBF1CStBmO×ClientStepAvgActi×newClientBMI:

g_(N1)=SBF2CIntO+SBF2CSO×ClientStepAvgActi+SBF2CBmO×newClientBMI+SBF2CStBmO×ClientStepAvgActi×newClientBMI:

2. The risk RCar2 estimated based on ClientMVAvgActi, ClientBMI,ClientCarFamily is:

$\begin{matrix}{{{RCar}\; 2} = {{CMBF}\left( {{ClientMVAvgActi},{ClientBMI},{ClientCarFamily}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{Y\; 2} \right)} & {{{{if}\mspace{14mu} {ClientCarFamily}} = Y};} \\{{logistic}\left( g_{N\; 2} \right)} & {{{{if}\mspace{14mu} {ClientCarFamily}} = N},}\end{matrix} \right.}\end{matrix}$

where CSBF( , , ) denotes RCar2 as a function of ClientMVAvgActi,ClientBMI, ClientCarFamily, ClientCarFamily, and:

g_(Y2)=MBF1CIntO+MBF1CStO×ClientMVAvgActi+MBF1CBmO×ClientBMI+MBF1CStBmO×ClientMVAvgActi×ClientBMI:

g_(N2)=MBF2CIntO+MBF2CStO×ClientMVAvgActi+MBF2CBmO×ClientBMI+MBF2CStBmO×ClientMVAvgActi×ClientBMI:

3. The risk RCar3 estimated based on ClientStepAvgActi, ClientBPR andClientGender is:

$\begin{matrix}{{{RCar}\; 3} = {{CSP}\left( {{ClientStepAvgActi},{ClientBPR},{ClientGender}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{{OY}\; 3} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{MY}\; 3} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{FY}\; 3} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{ON}\; 3} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}};} \\{{logistic}\left( g_{{MN}\; 3} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}};} \\{{logistic}\left( g_{{FN}\; 3} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}},}\end{matrix} \right.}\end{matrix}$

where CSP( , , ) denotes RCar3 as function of ClientStepAvgActi,ClientBPR, ClientGender, and

g _(OY3)=BpCYStIntO+BpCYSO×ClientStepAvgActi;

g _(MY3)=BpCYStIntM+BpCYStM×ClientStepAvgActi;

g _(FY3)=BpCYStIntF+BpCYStF×ClientStepAvgActi;

g _(ON3)=BpCNStIntO+BpCNSO×ClientStepAvgActi;

g _(MN3)=BpCNStIntM+BpCNStM×ClientStepAvgActi;

g _(FN3)=BpCNStIntF+BpCNStF×ClientStepAvgActi;

4. The risk RCar4 estimated based on newClientBMI, ClientBPR andClientGender is:

$\begin{matrix}{{{RCar}\; 4} = {{CBP}\left( {{newClientBMI},{ClientBPR},{ClientGender}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{{OY}\; 4} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{MY}\; 4} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{FY}\; 4} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{ON}\; 4} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}};} \\{{logistic}\left( g_{{MN}\; 4} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}};} \\{{logistic}\left( g_{{FN}\; 4} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}},}\end{matrix} \right.}\end{matrix}$

where CBP( , , ) denotes RCarA as a function of newClientBMI, ClientBPR,ClientGender, and

g _(OY4)=BCYBmIntO+BpCYBmO×newClientBMI;

g _(MY4)=BpCYbMIntM+BpCYBmM×newClientBMI;

g _(FY4)=BpCYBmIntF+BpCYBmF×newClientBMI;

g _(ON4)=BpBmIntO+BpCNBmO×newClientBMI;

g _(MN4)=BpCNBmIntM+BpCNBmM×newClientBMI;

g _(FN4)=BpCNBmIntF+BpCNBmF×newClientBMI;

5. The risk RCar5 estimated based on ClientMVAvgActi, ClientBPR andClientGender is:

$\begin{matrix}{{{RCar}\; 5} = {{CMP}\left( {{ClientMVAvgActi},{ClientBPR},{ClientGender}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{{OY}\; 5} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{MY}\; 5} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{FY}\; 5} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = Y}};} \\{{logistic}\left( g_{{ON}\; 5} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}};} \\{{logistic}\left( g_{{MN}\; 5} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}};} \\{{logistic}\left( g_{{FN}\; 5} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {ClientBPR}} = N}},}\end{matrix} \right.}\end{matrix}$

where CMP( , , ) denotes RCar5 as a function of ClientMVAvgActi,ClientBPR, ClientGender, and

g _(OY5)=BpCYMvIntO+BpCYMvO×ClientStepAvgActi;

g _(MY5)=BpCYMvIntM+BpCYMvM×ClientStepAvgActi;

g _(FY5)=BpCYMvIntF+BpCYMvF×ClientStepAvgActi;

g _(ON5)=BpCNMvIntO+BpCNMvO×ClientStepAvgActi;

g _(MN5)=BpCNMvIntM+BpCNMvM×ClientStepAvgActi;

g _(FN5)=BpCNMvIntF+BpCNMvF×ClientStepAvgActi;

6. The risk RCar6 estimated based on ClientWaist and ClientGender is:

$\begin{matrix}{{{RCar}\; 6} = {{CW}\left( {{ClientWaist},{ClientGender}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{O\; 6} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {NA}};} \\{{logistic}\left( g_{M\; 6} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = M};} \\{{logistic}\left( g_{F\; 6} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = F},}\end{matrix} \right.}\end{matrix}$

where CW( , , ) denotes RCar6 as a function of ClientWaist,ClientGender, and

g _(O6)=WCIntO+WCWcO×ClientWaist

g _(M6)=WCIntM+WCWcM×ClientWaist

g _(F6)=WCIntF+WCWcF×ClientWaist.

7. The formula of CAvgRiskSB is given by:

$\begin{matrix}{{CAvgRiskSB} = {{CSB}\left( {{ClientStepAvgActi},{ClientBMI},{ClientGender}} \right)}} \\{= \left\{ \begin{matrix}{{logistic}\left( g_{O\; 7} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = {NA}};} \\{{logistic}\left( g_{M\; 7} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = M};} \\{{logistic}\left( g_{F\; 7} \right)} & {{{{if}\mspace{14mu} {ClientGender}} = F},}\end{matrix} \right.}\end{matrix}$

where CSB( , , ) denotes CAvgRiskSB as a function of ClientMVAvgActi,ClientBMI+ClientGender, and

g_(O7)=SBCIntO+SBCStO×ClientStepAvgActi+SBCBmO×newClientBMI+SBCStBmO×ClientStepAvgActi×newClientBMI:

g_(M7)=SBCIntM+SBCStM×ClientStepAvgActi+SBCBmM×newClientBMI+SBCStBmM×ClientStepAvgActi×newClientBMI:

g_(F7)=SBCIntF+SBCStF×ClientStepAvgActi+SBCBmF×newClientBMI+SBCStBmF×ClientStepAvgActi×newClientBMI.

The formulae for calculating the cardiovascular disease subscoresS_(cardio) and S_(cardio(AG)) are given by:

$S_{cardio} = \left\{ {{\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 594} = {NO}};} \\{{FWellScoreCardio}\;} & {{{if}\mspace{14mu} N\; 594} = {{{YES}\mspace{14mu} {and}\mspace{14mu} L\; 594} =}} \\\; & {{``{{Cardio}\left( {{compare}\mspace{14mu} {to}\mspace{14mu} {same}\mspace{14mu} {group}} \right)}"};} \\{FWellScoreCardioHel} & {{{if}\mspace{14mu} N\; 594} = {{{YES}\mspace{14mu} {and}\mspace{14mu} L\; 594} =}} \\\; & {{``{{Cardio}\left( {{compare}\mspace{14mu} {to}\mspace{14mu} {Healthy}\mspace{14mu} {group}} \right)}"};} \\{FWellScoreCardioSB} & {{{if}\mspace{14mu} N\; 594} = {{{YES}\mspace{14mu} {and}\mspace{14mu} L\; 594} =}} \\\; & {{``{{{{Cardio}\mspace{14mu} {JUST}\mspace{14mu} {Based}\mspace{14mu} {on}\mspace{14mu} {Steps}}\&}\mspace{14mu} {BMI}}"}.}\end{matrix}S_{{cardio}{({AG})}}} = \left\{ \begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 594} = {NO}};} \\{FWellScoreCardioAGen} & {{{if}\mspace{14mu} N\; 594} = {{{YES}\mspace{14mu} {and}\mspace{14mu} L\; 594} =}} \\\; & {{``{{Cardio}\left( {{compare}\mspace{14mu} {to}\mspace{14mu} {same}\mspace{14mu} {group}} \right)}"};} \\{FWellScoreCardioHelAGen} & {{{if}\mspace{14mu} N\; 594} = {{{YES}\mspace{14mu} {and}\mspace{14mu} L\; 594} =}} \\\; & {{``{{Cardio}\left( {{compare}\mspace{14mu} {to}\mspace{14mu} {Healthy}\mspace{14mu} {group}} \right)}"};} \\{FWellScoreCardioAGenSB} & {{{if}\mspace{14mu} N\; 594} = {{{YES}\mspace{14mu} {and}\mspace{14mu} L\; 594} =}} \\\; & {{``{{{{Cardio}\mspace{14mu} {JUST}\mspace{14mu} {Based}\mspace{14mu} {on}\mspace{14mu} {Steps}}\&}\mspace{14mu} {BMI}}"}.}\end{matrix} \right.} \right.$

The formula of FWellScoreCardio is given by:

${FWellScoreCardio} = \left\{ \begin{matrix}{NULL} & {{{{if}\mspace{14mu} {ClientCardio}} = {Y\mspace{14mu} {or}}}\mspace{14mu}} \\\; & {{{ClientCardio} = {FALSE}};} \\y_{0} & {{{{if}\mspace{14mu} {ClientCardio}} = {N\mspace{14mu} {and}}}\mspace{11mu}} \\\; & {\; {{{CAvgRisk} < x_{c\; 0}};}} \\{f_{c}\left( {{CAvgRisk};{SC}_{cardio}} \right)} & {{{if}\mspace{14mu} {ClientCardio}} = {N\mspace{14mu} {and}}} \\\; & {{x_{c\; 0} \leq {CAvgRisk} \geq x_{c\; 5}};} \\y_{5} & {{{if}\mspace{14mu} {ClientCardio}} = {N\mspace{14mu} {and}}} \\\; & {\mspace{14mu} {{{CAvgRisk} > x_{c\; 5}},}}\end{matrix} \right.$

where SC_(cardio)={(x_(c0), y₀), (x_(c1),y1), (x_(c2), y2), (x_(c3),y3), (x_(c4), y4), (x_(c5), y₅)} and

x_(c0)=CSBMP100;

x_(c1)=CSBMP80;

x_(c2)=CSBMP70;

x_(c3)=CSBMP50;

x_(c4)=CSBMP20;

x_(c5)=CSBMP0;

y₀=SCExcellent;

y₁=SCVgood;

y₂=SCGood;

y₃=SCFair;

y₄=SCPoor;

y₅=SCO

FIG. 16 shows the pattern of the curve function to be applied to theclient's average risk of cardiovascular diseases CAvgRisk to obtain thecardiovascular diseases subscore where the (x_(c0), y₀) and (x_(c5),y₅), are (0.005, 100) and (0.15,0), respectively. The y₁, y₂, y₃, y₄ arefixed to be 86, 73, 61, 49, respectively. The x_(c1), x_(c2), x_(c3),x_(c4) are calculated according to the population data and the client'sage and gender. To do so, in an embodiment, four levels of numericdeciles (20%, 50%, 70%, and 80%) for each of ClientStepAvgActi,ClientMVAvgActi, ClientBMI and Client Waist with the given gender andage of the client. The deciles are ranked in the following “goodnessorder”:

Quartiles in goodness order Variables Poor Fair Good VerygoodClientStepAvgActi st₁ st₂ st₃ st₄ ClientBMI bmi₁ bmi₂ bmi₂ bmi₁ClientWaist wai₁ wai₃ wai₂ wai₁ ClientMVAvgActi mv₁ mv₂ mv₂ mv₄where the deciles of ClientStepAvgActi, ClientMVAvgActi, ClientBMI andClientWaist are denoted as st, mv, bmi and wai, respectively. Theirsubscripts 1,2,3 and 4 are representing 20%, 50%, 70% and 80%,respectively.

For each of the models, four risks for cardiovascular diseases arecalculated, ranging from poor, fair, good, and very good by applying thesame calculation as was used in calculating RCar1, RCar2, RCar3, RCar4,RCar5, and RCar6 to those deciles, that is, all numeric variables can bereplaced with corresponding deciles. The categorical variables remainthe same.

1. The model based on ClientStepAvgActi, ClientBMI, and ClientCarFamilyis:

R_(car1,poor)=CSBF(st₁, bmi₄, ClientCarFamily)

R_(car1,fair)=CSBF(st₂, bmi₃, ClientCarFamily)

R_(car1,good)=CSBF(st₃, bmi₂, ClientCarFamily)

R_(car1,excellent)=CSBF(st₄, bmi₁, ClientCarFamily)

2. The model based on ClientMVAvgActi, ClientBMI, and ClientGender is:

R_(car2,poor)=CMBF(mv₁, bm₄, ClientCarFamily)

R_(car2,fair)=CMBF(mv₂, bm₃, ClientCarFamily)

R_(car2,good)=CMBF(mv₃, bm₂, ClientCarFamily)

R_(car2,excellent)=CMBF(mv₄, bm₁, ClientCarFamily)

3. The model based on ClientStepAvgActi, ClientBPR, and ClientGender is:

R_(car3,poor)=CSP(st₁, ClientBPR, ClientGender)

R_(car3,fair)=CSP(st₂, ClientBPR, ClientGender)

R_(car3,good)=CSP(st₃, ClientBPR, ClientGender)

R_(car3,excellent)=CSP(st₄, ClientBPR, ClientGender)

4. The model based on ClientBMI, ClientBPR, and ClientGender is:

R_(car4,poor)=CBP(bmi₄, ClientBPR, ClientGender)

R_(car4,fair)=CBP(bmi₃, ClientBPR, ClientGender)

R_(car4,good)=CBP(bmi₂, ClientBPR, ClientGender)

R_(car4,excellent)=CBP(bmi₁, ClientBPR, ClientGender)

5. The model based on ClientMVAvgActi and ClientBPR and ClientGender is:

R_(car5,poor)=CMP(mv₁, ClientBPR, ClientGender)

R_(car5,fair)=CMP(mv₂, ClientBPR, ClientGender)

R_(car5,good)=CMP(mv₃, ClientBPR, ClientGender)

R_(car5,excellent)=CMP(mv₄, ClientBPR, ClientGender)

6. The model based on ClientWaist and ClientGender is:

R_(car6,poor)=CW(wai₄, ClientGender)

R_(car6,fair)=CW(wai₃, ClientGender)

R_(car6,good)=CW(wai₂, ClientGender)

R_(car6,excellent)=CW(wai₁, ClientBPR, ClientGender)

After the risks have been calculated, they can be averaged to obtainx_(c1), x_(c2), x_(c3), x_(c4):

${x_{c\; 1} = {\frac{1}{6}{\sum\limits_{i = 1}^{6}\; R_{{cari},{excellent}}}}};$${x_{c\; 2} = {\frac{1}{6}{\sum\limits_{i = 1}^{6}\; R_{{cari},{good}}}}};$${x_{c\; 3} = {\frac{1}{6}{\sum\limits_{i = 1}^{6}\; R_{{cari},{fair}}}}};$$x_{c\; 4} = {\frac{1}{6}{\sum\limits_{i = 1}^{6}\; R_{{cari},{poor}}}}$

7. To calculate FWellScoreCardioSB and FWellScoreCardioAGenSB, thefollowing estimated deciles of cardio risks based on steps and BMI mustbe calculated:

R_(car7,poor)=CSB(st₁, bmi₄, ClientGender)

R_(car7,fair)=CSB(st₂, bmi₃, ClientGender)

R_(car7,good)=CSB(st₃, bmi₂, ClientGender)

R_(car7,excellent)=CSB(st₄, bmi₁, ClientGender)

Let f₂( , , , ) denote the CAvgRisk as a function of ClientStepAvgActi,ClientMVAvgActi, ClientBMI, and ClientWaist. That is:

CAvgRisk=f₂(ClientStepAvgActi, AvgMVGenAgeActi, ClientcBMI, ClientWaist)

Then the formula of CAvgRiskGenAge is:

CAvgRiskGenAge=f₂(AvgStepGenAgeActi, AvgMVGenAgeActi, ClientBMIGenAge,AvgWaistGenAge)

where AvgStepGenAgeActi (the average daily steps taken of the generalpopulation, separated into various age brackets and genders),ClientMVGenAge (the average daily minutes of MV), ClientBMIGenAge (theaverage BMI) and AvgWaistGenAge (the average waist size) are given by:

${AvgStepGenAgeActi} = \left\{ \begin{matrix}9224 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\8830 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\8941 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\8264 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\7368 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\6237 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\9848 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\9422 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\9837 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\8687 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\7878 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\6906 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\8534 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\8280 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\8010 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\7867 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\6880 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\5677 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70.}}\end{matrix} \right.$

which are the mean average daily step counts for each age and genderbracket.

${AvgMVGenAgeActi} = \left\{ \begin{matrix}27.261345 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\22.877719 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\21.183897 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\18.057031 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\13.165338 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\9.948715 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\29.981944 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\25.991409 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\24.419647 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\18.855232 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\13.874513 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\11.686853 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\24.308956 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\20.014397 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\17.935525 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\17.30886 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\12.49993 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\8.529676 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70.}}\end{matrix} \right.$

which are the mean average daily minutes of MV for each age and genderbracket.

${ClientBMIGenAge} = \left\{ {\begin{matrix}26 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\25 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\27 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\29 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\29 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\29.23 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\27 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\27 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\end{matrix}.} \right.$

which are the mean average BMI for each age and gender bracket.

${AvgWaistGenAge} = \left\{ {\begin{matrix}85.53 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\89.95 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\92.88 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\95.34 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\97.38 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\96.39 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\87.24 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\94 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\96.85 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\101.26 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\102.6 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\101.57 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\83.61 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 20} \leq {Clientage} < 30}};} \\85.98 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\88.79 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\89.79 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\92.45 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\92.05 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\end{matrix}.} \right.$

which are the mean average waist sizes for each age and gender bracket.

The formula for FWellScoreCardioAGen is given by:

${FWellScoreCardioAGen} = \left\{ {\begin{matrix}{NULL} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = Y} \\{{{{or}\mspace{14mu} {ClientCardio}} = {FALSE}};}\end{matrix} \\_{0} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{{and}\mspace{14mu} {CAvgRisk}} < x_{c\; 0}};}\end{matrix} \\{f_{c}\left( {{CAvgRiskGenAge};{SC}_{cardio}} \right)} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{{and}\mspace{14mu} x_{c\; 0}} \leq {CAvgRisk} \geq x_{c\; 5}};}\end{matrix} \\_{5} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{and}\mspace{14mu} {CAvgRisk}} > x_{c\; 5}}\end{matrix}\end{matrix},} \right.$

The FellWellScoreCardioSB and FWellScoreCardioAGenSB is obtained by:

FWellScoreCardioSB=f_(c)(CAvgRiskSB; SC_(cardio,SB));

FWellScoreCardioAGenSB=f_(c)(RCarStBMIGenAge; SC_(cardio,SB)),

where SC_(cardio,SB)={(x_(csb0), y₀), (x_(csb1), y₁), (x_(csb2), y₂),(x_(cbs3), y₃), (x_(csb4), y₄), (x_(csb5), y₅)} and

x_(csb0)=CarStBm100;

x_(csb1)=CarStBm80;

x_(csb2)=CarStBm70;

x_(csb3)=CarStBm50;

x_(csb4)=CarStBm20;

x_(csb5)=CarStBm0;

y₀, y₁, y₂, y₃, y₄, y₅ are obtained as explained in the calculation ofFWellScoreCardio. The x_(csb0) and x_(csb5) are set to be 0.005 and0.24, respectively. X_(csb1), x_(csb2), x_(csb3), x_(csb4) arecalculated by the following formulae:

x_(csb1)=R_(car7,excellent);

x_(csb2)=R_(car7,good);

x_(csb3)=R_(car7,fair);

x_(csb4)=R_(car7,poor);

The RCarStBMIGenAge is given by:

RCarStBHIGenAge=CSB(AvgStepGenAgeActi, ClientBMIGenAge, ClientGender)

The subscores compared to healthy people in the population is calculatedby:

${FWellScoreCardioHel} = \left\{ {\begin{matrix}{NULL} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = Y} \\{{{{or}\mspace{14mu} {ClientCardio}} = {FALSE}};}\end{matrix} \\_{0} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{{and}\mspace{14mu} {CAvgRisk}} < x_{c\; 0}};}\end{matrix} \\\begin{matrix}{f_{c}\left( {{CAvgRisk};{SC}_{{cardio},\; H}} \right)} \\\;\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{{and}\mspace{14mu} x_{c\; 0}} \leq {CAvgRisk} \geq x_{c\; 5}};}\end{matrix} \\_{5} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{and}\mspace{14mu} {CAvgRisk}} > x_{c\; 5}}\end{matrix}\end{matrix},{{FWellScoreCardioHelAGen} = \left\{ {\begin{matrix}{NULL} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = Y} \\{{{{or}\mspace{14mu} {ClientCardio}} = {FALSE}};}\end{matrix} \\_{0} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{{and}\mspace{14mu} {CAvgRisk}} < x_{c\; 0}};}\end{matrix} \\{f_{c}\left( {{CAvgRiskGenAge};{SC}_{{cardio},\; H}} \right)} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{{and}\mspace{14mu} x_{c\; 0}} \leq {CAvgRisk} \geq x_{c\; 5}};}\end{matrix} \\\begin{matrix}_{5} \\\;\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientCardio}} = N} \\{{{and}\mspace{14mu} {CAvgRisk}} > x_{c\; 5}}\end{matrix}\end{matrix},} \right.}} \right.$

where SC_(cardio,H)=|{(x_(ch0), y₀), (x_(ch1), y₁), (x_(ch2), y₂),(x_(ch3), y₃), (x_(ch4), y₄), (x_(ch5), y₅)}, x_(ch0)=0.005,x_(ch5)=0.15 and x_(ch1), c_(ch2), x_(ch3), x_(ch4) are calculated inthe same way as x_(c1), c_(c2), x_(c3), x_(c4), were calculated, butwith fixed values for ClientBPR and ClientCarFamily such thatclientBPR=N; ClientCarFamily=N.

Diabetes

As above, other disease risk Health Subscores, such as diabetes, may bedetermined according to embodiments herein. Briefly, a digital biomarkerfor diabetes risk may be generated using at least some or all of theinput information shown in FIG. 17. Additionally, population dataregarding steps, MV activity time, BMI, and waist can be used as abaseline with which to compare the client. As with the cardiovasculardisease subscore, logistic models and curve functions can be used tocalculate the client's diabetes risk DAvgRisk. A curve function can thenbe applied to DAvgRisk to obtain the diabetes subscore S_(diabet), usingvarious logistic models requiring predetermined intercepts andcoefficients. As above, the formulae for calculating the diabetessubscores S_(diabet) and S_(diabet(AG)) are given by:

$S_{diabet} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 595} = {NO}};} \\{FWellScoreDiabeSB} & {{{if}\mspace{14mu} N\; 595} = {{YES}\mspace{14mu} {and}}} \\\; & {{L\; 595} = {``{{Diabetes}\mspace{14mu} {Based}\mspace{14mu} {on}}}} \\\; & {{{{{{JUST}\mspace{14mu} {Steps}}\&}\mspace{11mu} {BMI}}\;"};} \\{FWellScoreDiabe} & {{{if}\mspace{14mu} N\; 595} = {{YES}\mspace{14mu} {and}}} \\\; & {{L\; 595} = {``{{Diabetes}\mspace{14mu} {Based}\mspace{14mu} {on}\mspace{14mu} {All}\mspace{14mu} {Factors}}"}}\end{matrix},{S_{{diabet}\mspace{11mu} {({AG})}} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 595} = {NO}};} \\{FWellScoreDiabeSBAGen} & \begin{matrix}{{{if}\mspace{14mu} N\; 595} = {{YES}\mspace{14mu} {and}}} \\{{L\; 595} = {``{{Diabetes}\mspace{14mu} {Based}\mspace{14mu} {on}}\mspace{14mu}}} \\{{{{{{JUST}\mspace{14mu} {Steps}}\&}\mspace{11mu} {BMI}}\;"};}\end{matrix} \\{FWellScoreDiabeAGen} & \begin{matrix}{{{if}\mspace{14mu} N\; 595} = {{YES}\mspace{14mu} {and}}} \\{{L\; 595} = {``{{Diabetes}\mspace{14mu} {Based}\mspace{14mu} {on}}}} \\{{{All}\mspace{14mu} {Factors}}"}\end{matrix}\end{matrix}.} \right.}} \right.$

where FWellScoreDiabe=f_(c)(DAvgRisk; SD), where SD={(x_(d0), y₀),(x_(d1), y₁), (x_(d2), y₂), (x_(d3), y₃), (x_(d4), y₄), (x_(d5), y₅)},

-   x_(d0)=DSBMP100;-   x_(d1)=DSBMP80;-   x_(d2)=DSBMP70;-   x_(d3)=DSBMP50;-   x_(d4)=DSBMP20;-   x_(d5)=DSBMP0;

where the (x_(d0), y₀) and (x_(d5), y₅) are (0.015, 100) and (0.153, 0),respectively. The y₁, y₂, y₃, y₄, are fixed to be 86, 73, 61, and 49,respectively. The x_(d1), x_(d2), x_(d3), x_(d4) are calculatedaccording to the population data and the client's age and gender, as wasdone for x_(c1), c_(c2), x_(c3), x_(c4) in the cardiovascular diseasesection above. A plot showing the pattern of the curve function to beapplied to the client's average risk of diabetes DAvgRisk to obtain thediabetes subscore is shown in FIG. 18. For each one of the models, fourrisks for diabetes are calculated ranging from poor, fair, good, andvery good by applying the same calculation as used to calculate RDia1,RDia2, RDia3, and RDia4 to those deciles. As with cardiovascular riskabove, all numeric variables can be replaced with corresponding deciles.The categorical variables remain the same.

Arthritis

As above, other disease risk Health Subscores, such as arthritis, may bedetermined according to embodiments herein. Briefly, a digital biomarkerof arthritis risk may be generated using at least some or all of theincoming wellness information including, without limitation, age,gender, waist in cm, current BMI, daily average steps, daily average MVactivity in minutes, medical diagnosis on arthritis, treatment thathelps arthritis, etc. Additionally, population data regarding steps, MVactivity time, BMI, and waist can be used as a baseline with which tocompare the client. As with the other disease subscores, logistic modelscan be used to calculate the client's arthritis risk AAvgRisk. Logisticmodels requiring various predetermined intercepts and coefficients areused. A curve function can then be applied to AAvgRisk to obtain thearthritis subscore Sarthn. The formulae for calculating the arthritissubscores S_(arthri) and S_(arthri(AG)) are given by:

$S_{arthri} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 596} = {NO}};} \\{f_{c}\left( {{AAvgRisk};{SA}} \right)} & {{{if}\mspace{14mu} N\; 596} = {YES}}\end{matrix},{S_{{arthri}\mspace{11mu} {({AG})}} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 596} = {NO}};} \\{f_{c}\left( {{AAvgRiskAgeGen};{SA}} \right)} & {{{if}\mspace{14mu} N\; 596} = {YES}}\end{matrix},} \right.}} \right.$

where SA={(x_(a0), y₀), (x_(a1), y₁), (x_(a2), y₂), (x_(a3), y₃),(x_(a4), y₄), (x_(a5), y₅)}.

x_(a0)=ASBMP100;

x_(a1)=ASBMP80;

x_(a2)=ASBMP70;

x_(a3)=ASBMP50;

x_(a4)=ASBMP20;

x_(a5)=ASBMP0;

where the (x_(a0), y₀) and (x_(a5), y₅) are (0.015, 100) and (0.4, 0)respectively. The y₁, y₂, y₃, y₄are fixed to be 86, 73, 61, 49respectively. The x_(a1), x_(a2), x_(a3), x_(a4) are calculatedaccording to the population data and the client's age and gender, as wasdone for x_(c1), c_(c2), x_(c3), x_(c4) in the cardiovascular diseasesection above. For each one of the models, four risks of arthritis arecalculated ranging from poor, fair, good, and very good by applying thesame calculation as in calculating RArt1, RArt2, RArt3 to those deciles.As with cardiovascular risk and diabetes risk above, all numericvariables can be replaced with corresponding deciles. The categoricalvariables remain the same.

Lung Disease

As above, other disease risk Health Subscores, such as lung disease, maybe determined according to embodiments herein. Briefly, a digitalbiomarker of lung disease risk may be generated using at least some orall of the incoming wellness information including, without limitation,age, gender, waist in cm, current BMI, daily average steps, dailyaverage MV activity in minutes, medical diagnosis on lung disease,treatment that helps lung disease, etc. Additionally, population dataregarding steps, MV activity time, BMI, and waist can be used as abaseline with which to compare the client. As above, logistic models andcurve functions can be used to calculate the client's lung disease riskLAvgRisk. A curve function can then be applied to LAvgRisk to obtain thelung disease subscore S_(lung), using various logistic models requiringpredetermined intercepts and coefficients. The risk of lung diseaseLAvgRisk can be obtained by:

LAvgRisk=1/3(RLun1+RLun2+RLun3)

where the RLun1, RLun2, RLun3 are the risks calculated from three modelswith a plurality of

variables/factors. The formulae for calculating the lung diseasesubscores S_(lung) and S_(lung(AG)) are given by:

$S_{lung} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 597} = {NO}};} \\{f_{c}\left( {{LAvgRisk};{SL}} \right)} & {{{if}\mspace{14mu} N\; 597} = {YES}}\end{matrix},{S_{{lung}\mspace{11mu} {({AG})}} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 597} = {NO}};} \\{f_{c}\left( {{LAvgRiskAgeGen};{SL}} \right)} & {{{if}\mspace{14mu} N\; 597} = {YES}}\end{matrix},} \right.}} \right.$

where SL={(x_(l0), y₀), (x_(l1), y₁), (x_(l2), y₂), (x_(l3), y₃),(x_(l4), y₄), (x_(l5), y₅)}. And

x_(l0)=LSBMP100;

x_(l1)=LSBMP80;

x_(l2)=LSBMP70;

x_(l3)=LSBMP50;

x_(l4)=LSBMP20;

x_(l5)=LSBMP0;

where the (x_(l0),y₀) and (x_(l5), y₅) are (0.015, 100) and (0.18, 0),respectively. The y₁, y₂, y₃, y₄are fixed to be 86, 73, 61, 49respectively. The x_(l1), x_(l2), x_(l3), x_(l4) are calculatedaccording to the population data and the client's age and gender, as wasdone for x_(c1), c_(c2), x_(c3), x_(c4) in the cardiovascular diseasesection above. For each one of the models, four risks of arthritis arecalculated ranging from poor, fair, good, and very good by applying thesame calculation as in calculating Rlun1, Rlun2, Rlun3 to those deciles.As with cardiovascular risk, diabetes risk, and arthritis risk above,all numeric variables can be replaced with corresponding deciles. Thecategorical variables may remain the same.

Body Pain

As above, other disease risk health subscores, such as body pain, backpain (e.g., lower back pain), may be determined according to embodimentsherein. Briefly, lower back pain risk health subscores may be generatedusing at least some or all of input information including, withoutlimitation, age, gender, waist in cm, current BMI, daily average steps,daily average MV activity in minutes, medical diagnosis on lower backpain, treatment that helps lower back pain, etc. Additionally,population data regarding steps, MV activity time, BMI, and waist can beused as a baseline with which to compare the client. As above,population data regarding steps, MV activity time, BMI, and waist can beused as a baseline with which to compare the client. As with the otherdisease risk subscores, logistic models and curve functions can be usedto calculate the client's lower back pain risk BAvgRisk. A curvefunction can then be applied to BAvgRisk to obtain the arthritissubscore Sibpam, using various logistic models requiring predeterminedintercepts and coefficients. The risk of lower back pain BAvgRisk can beobtained by:

BAvgRisk=1/3(RLbp1+RLbp2+RLbp3)

where the RLbp1, RLbp2, RLbp3 are the risks calculated from the logisticmodels with at least three groups of variables/factors. The formulae forcalculating the lower back pain subscores S_(lbpain) and S_(lbpain(AG))are given by:

$S_{lbpain} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 598} = {NO}};} \\{f_{c}\left( {{BAvgRisk};{SB}} \right)} & {{{if}\mspace{14mu} N\; 598} = {YES}}\end{matrix},{S_{{lbpain}\mspace{11mu} {({AG})}} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} N\; 598} = {NO}};} \\{f_{c}\left( {{BAvgRiskAgeGen};{SB}} \right)} & {{{if}\mspace{14mu} N\; 598} = {YES}}\end{matrix},} \right.}} \right.$

where SB={(x_(b0), y₀), (x_(b1), y₁), (x_(b2), y₂), (x_(b3), y₃),(x_(b4), y₄), (x_(b5), y₅)}. And

x_(b0)=BSBMP100;

x_(b1)=BSBMP80;

x_(b2)=BSBMP70;

x_(b3)=BSBMP50;

x_(b4)=BSBMP20;

x_(b5)=BSBMP0;

where the (x_(b0),y₀) and (x_(b5), y₅) are (0.015, 100) and (0.18, 0),respectively. The y₁, y₂, y₃, y₄are fixed to be 86, 73, 61, 49respectively. The x_(lb1), x_(lb2), x_(lb3), x_(lb4) are calculatedaccording to the population data and the client's age and gender, as wasdone for x_(c1), c_(c2), x_(c3), x_(c4) in the cardiovascular diseasesection above. For each one of the models, four risks of arthritis arecalculated ranging from poor, fair, good, and very good by applying thesame calculation as in calculating RLbp1, RLbp2, RLbp3 to those deciles.As with cardiovascular risk and diabetes risk above, all numericvariables can be replaced with corresponding deciles. The categoricalvariables remain the same.

Mental Health (or “VivaMind Score”)

According to further embodiments herein, the present systems and methodsmay also provide Health Subscore indicative of the individual or group'smental health (referred to as a “VivaMind Score”; S_(VM)). Herein, aVivaMind Health Subscore may be generated using incoming wellnessinformation relating to different mental health metrics including,without limitation, stress level (S_(sts)), level of happiness (S_(lh)),depression (S_(dep)), and model based happiness analysis (S_(ha)), ascompared against the general population. VivaMind subscores relating tothe general population for any given age bracket and gender (S_(VM(AG)))may comprise the average of S_(sts(AG)), S_(lh(AG)), S_(dep(AG)), andS_(ha(AG)). By way of example, the presents methods of calculatingsubscores stress level (S_(sts)), level of happiness (S_(lh)),depression (S_(dep)), and model based happiness analysis (S_(ha)) aredescribed below.

Stress

A stress subscore S_(sts) may be generated based on the inputs of astress contribution indicator (D610), which is a yes/no value thatdetermines whether the stress subscore contributes to the calculation ofthe client's overall wellness score, and the client's rating of his/herstress level (StressLevel). The possible answers for StressLevel are:“not at all stressful”, “not very stressful”, “a bit stressful”, “quitea bit stressful”, and “extremely stressful”.

The stress subscore S_(sts) is given by:

$S_{sts} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} D\; 610} = {NO}};} \\100 & \begin{matrix}{{{if}\mspace{14mu} D\; 610} = {{YES}\mspace{14mu} {and}}} \\{{{StressLevel} = {{NOT}\mspace{14mu} {AT}\mspace{14mu} {ALL}\mspace{14mu} {STRESSFUL}}};}\end{matrix} \\80 & \begin{matrix}{{{if}\mspace{14mu} D\; 610} = {{YES}\mspace{14mu} {and}}} \\{{{StressLevel} = {{NOT}\mspace{14mu} {VERY}\mspace{14mu} {STRESSFUL}}};}\end{matrix} \\65 & \begin{matrix}{{{{if}\mspace{14mu} D\; 610} = {{YES}\mspace{14mu} {and}}}\mspace{14mu}} \\{{{StressLevel} = {A\mspace{14mu} {BIT}\mspace{14mu} {STRESSFUL}}};}\end{matrix} \\50 & \begin{matrix}{{{{if}\mspace{14mu} D\; 610} = {{YES}\mspace{14mu} {and}}}\mspace{14mu}} \\{{{StressLevel} = {{QUITE}\mspace{14mu} A\mspace{14mu} {BIT}\mspace{14mu} {STRESSFUL}}};}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} D\; 610} = {{YES}\mspace{14mu} {and}}} \\{{StressLevel} = {{EXTREMELY}\mspace{14mu} {STRESSFUL}}}\end{matrix}\end{matrix};} \right.$

The subscore S_(sts(AG)) is calculated based on contribution indicator(D610), the client's age (Clientage), and population distribution amongthe five levels of stress in various age categories. Such data can betabulated and stored, such as in an Excel™ spreadsheet as shown, forexample, in FIG. 19.

If D610=YES, the formula of S_(sts(AG)) is

$S_{{sts}\; {({AG})}} = \left\{ {\begin{matrix}\begin{matrix}{{100 \times C\; 2276} + {85 \times C\; 2277} +} \\{{65 \times C\; 2278} + {45 \times C\; 2279}}\end{matrix} & {{{{if}\mspace{14mu} {Clientage}} < 30};} \\\begin{matrix}{{100 \times D\; 2276} + {85 \times D\; 2277} +} \\{{65 \times D\; 2278} + {45 \times D\; 2279}}\end{matrix} & {{{{if}\mspace{14mu} 30} \leq {Clientage} < 40};} \\\begin{matrix}{{100 \times E\; 2276} + {85 \times E\; 2277} +} \\{{65 \times E\; 2278} + {45 \times E\; 2279}}\end{matrix} & {{{{if}\mspace{14mu} 40} \leq {Clientage} < 50};} \\\begin{matrix}{{100 \times F\; 2276} + {85 \times F\; 2277} +} \\{{65 \times F\; 2278} + {45 \times F\; 2279}}\end{matrix} & {{{{if}\mspace{14mu} 50} \leq {Clientage} < 60};} \\\begin{matrix}{{100 \times G\; 2276} + {85 \times G\; 2277} +} \\{{65 \times G\; 2278} + {45 \times G\; 2279}}\end{matrix} & {{{if}\mspace{14mu} {Clientage}} \geq 60}\end{matrix}.} \right.$

where C-G combined with numbers 2276-2279 refer to the cells of FIG. 19,containing percentages of the population who belong to each of the fivelevels of stress, divided into five age intervals (20-29, 30-39, 40-49,50-59, and 60+). If D610=NO, then S_(sts(AG))=NULL.

Happiness Level

A happiness subscore S_(lh) may be generated based on the inputs of ahappiness contribution indicator (D611), which is ayes/no value thatdetermines whether the hapiness subscore contributes to the calculationof the client's overall wellness score, and the client's rating ofhis/her happiness level (HappinessLevel). The possible answers forHappinessLevel are: “Happy and interested in life”, “Somewhat happy”,“Somewhat unhappy”, “Unhappy with little interest in life”, and “Sounhappy that life is not worthwhile”.

The happiness subscore S_(lh) is given by:

$S_{th} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} D\; 611} = {NO}};} \\100 & \begin{matrix}{{{if}\mspace{14mu} D\; 611} = {{YES}\mspace{14mu} {and}}} \\{{{HappinessLevel} = {{HAPPY}\mspace{14mu} {AND}\mspace{14mu} {INTERESTED}\mspace{14mu} {IN}\mspace{14mu} {LIFE}}};}\end{matrix} \\80 & \begin{matrix}{{{if}\mspace{14mu} D\; 611} = {{YES}\mspace{14mu} {and}}} \\{{{HappinessLevel} = {{SOMEWHAT}\mspace{14mu} {HAPPY}}};}\end{matrix} \\65 & \begin{matrix}{{{if}\mspace{14mu} D\; 611} = {{YES}\mspace{14mu} {and}}} \\{\; {{{HappinessLevel} = {{SOMEWHAT}\mspace{14mu} {UNHAPPY}}};}}\end{matrix} \\50 & \begin{matrix}{{{if}\mspace{14mu} D\; 611} = {{YES}\mspace{14mu} {and}}} \\{{HappinessLevel} = {{UNHAPPY}\mspace{14mu} {WITH}\mspace{14mu} {LITTLE}\mspace{14mu} {INTEREST}}} \\{{{IN}\mspace{14mu} {LIFE}};}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} D\; 611} = {{YES}\mspace{14mu} {and}}} \\{{HappinessLevel} = {{SO}\mspace{14mu} {UNHAPPY}\mspace{14mu} {THAT}\mspace{14mu} {LIFE}\mspace{14mu} {IS}\mspace{14mu} {NOT}}} \\{WORTHWHILE}\end{matrix}\end{matrix}.} \right.$

The subscore S_(lh(AG)) is calculated based on contribution indicator(D611), the client's age (Clientage), and population distribution amongthe top four happiness levels (with “Unhappy with little interest inlife” and “So unhappy that life is not worthwhile” levels beingcombined) in various age categories. Such data can be tabulated andstored, such as in an Excel™ spreadsheet as shown, for example, in FIG.20.

If D611= YES, the formula of S_(lh(AG)) is

$S_{{th}\; {({AG})}} = \left\{ {\begin{matrix}\begin{matrix}{{100 \times C\; 2288} + {75 \times}} \\{{C\; 2289} + {50 \times C\; 2290}}\end{matrix} & {{{{if}\mspace{14mu} 20} \leq {Clientage} < 33};} \\\begin{matrix}{{100 \times D\; 2288} + {75 \times}} \\{{D\; 2289} + {50 \times D\; 2290}}\end{matrix} & {{{{if}\mspace{14mu} 33} \leq {Clientage} < 46}\;;} \\\begin{matrix}{{100 \times E\; 2288} + {75 \times}} \\{{E\; 2289} + {50 \times E\; 2290}}\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {Clientage}} \geq 46} \\\;\end{matrix}\end{matrix};} \right.$

where C-E combined with numbers 2288-2290 refer to the cells of FIG. 20,containing percentages of the top four levels of happiness for the abovethree age intervals (20-32, 33-34, and 36+). If D611=NO, thenS_(lh(AG))=NULL.

Depression

As above, a depression subscore S_(dep) may be generated based upon atleast, one or more inputs including age, gender, current BMI, dailyaverage steps, medical diagnosis on depression, treatment that helpsdepression, etc. As with disease risk subscores above, logistic modelsand curve functions can be used to calculate the client's depressionrisk DepAvgRisk. A curve function can then be applied to DepAvgRisk toobtain the depression subscore S_(dep), utilizing various logisticsmodels requiring predetermined intercepts and coefficients. In someembodiments, the risk of depression can be calculated. In otherembodiments, the formulae for calculating the depression subscoresS_(dep) and S_(dep(AG)) are given by:

$S_{dep} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} D\; 613} = {NO}};} \\{f_{c}\left( {{DepAvgRisk};{S\mspace{11mu} {Dep}}} \right)} & {{{if}\mspace{14mu} D\; 613} = {YES}}\end{matrix},{S_{{dep}\mspace{11mu} {({AG})}} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} D\; 613} = {NO}};} \\{f_{c}\left( {{DepAvgRiskAgeGen};{S\mspace{11mu} {Dep}}} \right)} & {{{if}\mspace{14mu} D\; 613} = {YES}}\end{matrix},} \right.}} \right.$

where SDep={(x_(dep0), y₀), (x_(dep1),y₁), (x_(dep2), y₂), (x_(dep3),y₃), (x_(dep4), y₄), (x_(dep5), y₅)}, and

x_(dep0)=DepSBM100;

x_(dep1)=DepSBM80;

x_(dep2)=DepSBM70;

x_(dep3)=DepSBM50;

x_(dep4)=DepSBM20;

x_(dep5)=DepSBM0;

where the (x_(dep0),y₀) and (x_(dep5), y₅) are (0.015, 100) and (0.18,0), respectively. The y₁, y₂, y₃, y₄are fixed to be 86, 73, 61, 49respectively. The x_(dep1), x_(dep2), x_(dep3), x_(dep4) are calculatedaccording to the population data and the client's age and gender, as wasdone in the disease risk analysis described herein. For each of themodels, at least four risks for depression can be calculated, rangingfrom poor, fair, good, and very good by applying the same calculation aswas used in calculating DepAvgRisk to those deciles, that is, allnumeric variables can be replaced with corresponding deciles. Thecategorical variables remain the same.

Model Based Happiness

As above, a model based happiness subscore S_(ha(AG)) may be generatedbased upon at least, one or more inputs including gender, current BMI,daily average steps, daily average MV activity in minutes, etc. As withdepression and the disease risk subscores above, logistic models andvarious parameters/constants can be used to calculate the client's riskof unhappiness utilizing various logistic models requiring predeterminedintercepts and coefficients. The risk of unhappiness can be calculatedfrom the client's BMI UnHBMI, risk of unhappiness calculated fromclient's MV UnHMv, and risk of unhappiness calculated from client's stepcount UnHSt. The formulae for calculating the model based happinesssubscores S_(ha) and S_(ha(AG)) are given by:

$S_{happ} = \left\{ {{\begin{matrix}{NULL} & {{{{if}\mspace{14mu} D\; 612} = {NO}};} \\{\left( {{HapStScour} + {HapMvScour}} \right)/2} & {{{if}\mspace{14mu} D\; 612} = {YES}} \\\; & \begin{matrix}{{{and}\mspace{14mu} {HapBmiScour}} =} \\{{{Low}\mspace{14mu} {BMI}};}\end{matrix} \\{\begin{matrix}\left( {{HapStScour} + {HapMvScour} +} \right. \\\left. {HapBmiScour} \right)\end{matrix}/3} & {otherwise}\end{matrix}.S_{{ha}\mspace{11mu} {({AG})}}} = \left\{ {\begin{matrix}{NULL} & {{{{if}\mspace{14mu} D\; 612} = {NO}};} \\\frac{\begin{matrix}{{HapStScourGenAge} + {HapMvScourGenAge} +} \\{HapBmiScourGenAge}\end{matrix}}{3} & {{{if}\mspace{14mu} D\; 612} = {YES}}\end{matrix}.} \right.} \right.$

To calculate the subscores, three sub-subscores HapStScout, HapMvScour,and HapBmiScourt must be calculated:

${HapBmiScour} = \left\{ \begin{matrix}{{Low}\mspace{14mu} {BMI}} & {{{if}\mspace{14mu} {ClientBMI}} < 18.5} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 80}}{{{HapBMI}\mspace{11mu} 100} - {{HapBMI}\mspace{11mu} 80}}}} & \begin{matrix}{{{if}\mspace{14mu} 18.5} \leq {ClientBMI} \leq 31.17} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 5}}{{{HapBMI}\mspace{11mu} 80} - {{HapBMI}\mspace{11mu} 5}}}} & \begin{matrix}{{{if}\mspace{14mu} 31.17} < {ClientBMI} \leq 36.42} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 0}}{{{HapBMI}\mspace{11mu} 5} - {{HapBMI}\mspace{11mu} 0}}} & \begin{matrix}{{{if}\mspace{14mu} 36.42} < {ClientBMI} \leq 46.3} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} {ClientBMI}} > 46.3} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 80M}}{{{HapBMI}\mspace{11mu} 100M} - {{HapBMI}\mspace{11mu} 80M}}}} & \begin{matrix}{{{if}\mspace{14mu} 18.5} \leq {ClientBMI} \leq 30.86} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 5M}}{{{HapBMI}\mspace{11mu} 80M} - {{HapBMI}\mspace{11mu} 5M}}}} & \begin{matrix}{{{if}\mspace{14mu} 30.86} < {ClientBMI} \leq 36.42} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 0M}}{{{HapBMI}\mspace{11mu} 5M} - {{HapBMI}\mspace{11mu} 0M}}} & \begin{matrix}{{{if}\mspace{14mu} 36.42} < {ClientBMI} \leq 46.3} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} {ClientBMI}} > 46.3} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 80F}}{{{HapBMI}\mspace{11mu} 100F} - {{HapBMI}\mspace{11mu} 80F}}}} & \begin{matrix}{{{if}\mspace{14mu} 18.5} \leq {ClientBMI} \leq 31.48} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 5F}}{{{HapBMI}\mspace{11mu} 80F} - {{HapBMI}\mspace{11mu} 5F}}}} & \begin{matrix}{{{if}\mspace{14mu} 31.48} < {ClientBMI} \leq 36.42} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHBMI} - {{HapBMI}\mspace{11mu} 0F}}{{{HapBMI}\mspace{11mu} 5F} - {{HapBMI}\mspace{11mu} 0F}}} & \begin{matrix}{{{if}\mspace{14mu} 36.42} < {ClientBMI} \leq 46.3} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix} \\0 & \begin{matrix}{{{if}\mspace{14mu} {ClientBMI}} > 46.3} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix}\end{matrix} \right.$

Let HSB( , ) denote HapBmiScour as a mathematical function of ClientBMIand ClientGender, that is:

     HapBmiScour = HSB(ClientBMI, ClientGender)      Then     HapBmiScourGenAge = HSB(ClientBMIGenAge, ClientGender)${HapStScour} = \left\{ \begin{matrix}100 & \begin{matrix}{{{if}\mspace{14mu} {ClientStepAvgActi}} >} \\15000\end{matrix} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHSt} - {{HapSt}\mspace{11mu} 80}}{{{HapSt}\mspace{11mu} 100} - {{HapSt}\mspace{11mu} 80}}}} & \begin{matrix}{{{if}\mspace{14mu} 5223} \leq} \\{{ClientStepAvgActi} \leq 15000}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHSt} - {{HapSt}\mspace{11mu} 5}}{{{HapSt}\mspace{11mu} 80} - {{HapSt}\mspace{11mu} 5}}}} & \begin{matrix}{{{if}\mspace{14mu} 2000} \leq} \\{{ClientStepAvgActi} < 5223}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHSt} - {{HapSt}\mspace{11mu} 0}}{{{HapSt}\mspace{11mu} 5} - {{HapSt}\mspace{11mu} 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientStepAvgActi}} <} \\2000\end{matrix}\end{matrix} \right.$

Let HSS( , ) denote HapStScour as a mathematical function ofClientStepAvgActi and ClientGender. That is:

HapStScour=HSS(ClientStepAvgActi, ClientGender)

Then

     HapStScourGenAge = HSS(AvgStepGenAgeActi, ClientGender)${HapMvScour} = \left\{ \begin{matrix}100 & \begin{matrix}{{{if}\mspace{14mu} {ClientMV}} > 50} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 80}}{{{HapBMv}\mspace{11mu} 100} - {{HapBMv}\mspace{11mu} 80}}}} & \begin{matrix}{{{if}\mspace{14mu} 4.87} \leq {ClientMV} \leq 50} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 5}}{{{HapBMv}\mspace{11mu} 80} - {{HapBMv}\mspace{11mu} 5}}}} & \begin{matrix}{{{if}\mspace{14mu} 1} \leq {ClientMV} < 4.87} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 0}}{{{HapBMv}\mspace{11mu} 5} - {{HapBMv}\mspace{11mu} 0}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientMV}} < 1} \\{{{and}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix} \\\begin{matrix}100 \\\;\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientMV}} > 55} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 80\; M}}{{{HapBMv}\mspace{11mu} 100M} - {{HapBMv}\mspace{11mu} 80M}}}} & \begin{matrix}{{{if}\mspace{14mu} 6.41} \leq {ClientMV} \leq 55} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 5M}}{{{HapBMv}\mspace{11mu} 80M} - {{HapBMv}\mspace{11mu} 5M}}}} & \begin{matrix}{{{if}\mspace{14mu} 1.33} \leq {ClientMV} < 6.41} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 0M}}{{{HapBMv}\mspace{11mu} 5M} - {{HapBMv}\mspace{11mu} 0M}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientMV}} < 1.33} \\{{{and}\mspace{14mu} {ClientGender}} = M}\end{matrix} \\\begin{matrix}100 \\\;\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {ClientMV}} > 45} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix} \\{70 + {\left( {100 - 70} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 80F}}{{{HapBMv}\mspace{11mu} 100F} - {{HapBMv}\mspace{11mu} 80F}}}} & \begin{matrix}{{{if}\mspace{14mu} 3.68} \leq {ClientMV} \leq 45} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix} \\{50 + {\left( {69 - 50} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 5F}}{{{HapBMv}\mspace{11mu} 80F} - {{HapBMv}\mspace{11mu} 5F}}}} & \begin{matrix}{{{if}\mspace{14mu} 0.7} \leq {ClientMV} < 3.68} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix} \\{\left( {49 - 0} \right) \times \frac{1 - {UnHMv} - {{HapMv}\mspace{11mu} 0F}}{{{HapBMv}\mspace{11mu} 5F} - {{HapBMv}\mspace{11mu} 0F}}} & \begin{matrix}{{{if}\mspace{14mu} {ClientMV}} < 0.7} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix}\end{matrix} \right.$

where ClientMV=max(0, ClientMVAvgActi). Let HSM( , ) denote HapMvScouras a mathematical function of ClientMV and ClientGender. That is:

HapMvScour=HSM(ClientMV, ClientGender).

Then

HapMvScourGenAge=HSM(AvgMVGenAgeActi, ClientGender).

The other constants involved are listed as follows:

-   HapSt0[D1123]=1−C1123-   HapSt5[G1123]=1−F1123-   HapSt80[J1123]=1−I1123-   HapSt100[M1123]=1−L1123-   HapSt0M [D1129]=1−C1129-   HapSt5M[G1129]=1−F1129-   HapSt80M[J1129]=1−I1129-   HapSt100M[M1129]=1−L1129-   HapSt0F[D1135]=1−C1135-   HapSt5F[G1135]=1−F1135-   HapSt80F[J1135]=1−I1135-   HapSt100F[M1135]=1−L1135-   HapMv0[D1124]=1−C1124-   HapMv5[G1124]=1−F1124-   HapMv80[J1124]=1−I1124-   HapMv100[M1124]=1−L1124-   HapMv0M [D1130]=1−C1130-   HapMv5M[G1130]=1−F1130-   HapMv80M[J1130]=1−I1130-   HapMv100M[M1130]=1−L1130-   HapMv0F[D1136]=1−C1136-   HapMv5F[G1136]=1−F1136-   HapMv80F[J1136]=1−I1136-   HapMv100F[M1136]=1−L1136-   HapBMI0[D1125]=1−C1125-   HapBMI5[G1125]=1−F1125-   HapMBI80[J1125]=1−I1125-   HapBMI100[M1125]=1−L1125-   HapBMI0M [D1131]=1−C1131-   HapBMI5M[G1131]=1−F1131-   HapBMI80M[J1131]=1−I1131-   HapBMI100M[M1131]=1−L1131-   HapBMI0F[D1137]=1−C1137-   HapBMI5F[G1137]=1−F1137-   HapBMI80F[J1137]=1−I1137-   HapBMI100F[M1137]=1−L1137    where A-M combined with numbers 1122-1131 are references to cells in    a spreadsheet containing data in respect to happiness levels of the    general population given various values of average daily steps,    average daily MV, and BMI, as tabulated in FIG. 21. The data may be    separated into happiness levels for the male, female, and overall    population.

Overall Wellness

As above, the present computer-implemented system may further comprisethe processing of one or more of the at least one digital biomarkersubscores to generate at least one overall wellness scores, or “VivaMeScores”. According to embodiments herein, the VivaMe Score, denoted as Sfor convenience, may be generated using the weighted average of some orall of the Health Behavior, Disease Risk, and Viva-Mind HealthSubscores, although any other appropriate means of calculating theoverall may be used. For example,

S=0.4×S _(HB)+0.4×S _(DR)+0.2×S _(VM).

As above, the foregoing overall wellness score can be compared togeneral population information such as, for example, individuals orgroups of individuals that are similar in age, gender, etc. Accordingly,S_((AG)) may be generated as:

S _((AG))=0.4×S _(HB(AG))+0.4×S _(DR(AG))+0.2×S _(VM(AG))

where the VivaMe score may be obtained by taking the average value ofone or more Health Subscores, as:

S_(HB)=Round(Average(S_(stp), S_(mv), S_(slp), S_(wei), S_(bmi),S_(wst), S_(smk), S_(drk), S_(vr), S_(vt), S_(vm)),1).

The average score of the general population information having the sameage, gender, etc., may be compared as:

S_(HB(AG))=Round(Average(S_(stp(AG)), S_(mv(AG)), S_(slp(AG)),S_(wei(AG)), S_(bmi(AG)), S_(wst(AG)), S_(smk(AG)), S_(drk(AG)),S_(vr(AG)), S_(vt(AG)), S_(vm(AG))),1).

As would be understood, the function “Average” as described herein neednot require the presences of every component. In other words, thepresent systems may automatically ignore components that cannot beimplemented in numeric calculation, or it may automatically ignorescores/subscores because they have not been chosen to contribute to theoverall wellness information.

Mortality Rates

In addition to the foregoing, the present computer-implemented systemsmay be operative to generate, based upon one or more of the HealthSubscores, mortality rates associated with said one or more HealthSubscores. For example, mortality rates associated with the individualor group's age, cardiovascular disease, diabetes, etc., may bedetermined. The foregoing will now be described having regard to thefollowing examples.

Mortality Rate from Age

As an example, the overall probability of dying for someone in theclient's age range, the overall life expectancy at the client's age, andthe mortality rate in the client's age range are calculated afterobtaining the client's age (Clientage), client's gender (ClientGender),population data of life expectancy in various age ranges, and populationdata of probabilities of dying in various age ranges. As with all theother population data, the population data regarding life expectancy andprobabilities of dying can be tabulated and stored on the generalpopulation database. The probability of dying may be given according toAgerange1:

${{Agerange}\; 1} = \left\{ {\begin{matrix}{0 - 1} & {{{{if}\mspace{14mu} {Clientage}} \leq 1};} \\{2 - 5} & {{{{if}\mspace{14mu} 1} < {Clientage} \leq 5}\;;} \\{6 - 10} & {{{{if}\mspace{14mu} 5} < {Clientage} \leq 10}\;;} \\{11 - 15} & {{{{if}\mspace{14mu} 10} < {Clientage} \leq 15}\;;} \\{16 - 20} & {{{{if}\mspace{14mu} 15} < {Clientage} \leq 20}\;;} \\{21 - 25} & {{{{if}\mspace{14mu} 20} < {Clientage} \leq 25}\;;} \\{26 - 30} & {{{{if}\mspace{14mu} 25} < {Clientage} \leq 30}\;;} \\{31 - 35} & {{{{if}\mspace{14mu} 30} < {Clientage} \leq 35}\;;} \\{36 - 40} & {{{{if}\mspace{14mu} 35} < {Clientage} \leq 40}\;;} \\{41 - 45} & {{{{if}\mspace{14mu} 40} < {Clientage} \leq 45}\;;} \\{46 - 50} & {{{{if}\mspace{14mu} 45} < {Clientage} \leq 50}\;;} \\{51 - 55} & {{{{if}\mspace{14mu} 50} < {Clientage} \leq 55}\;;} \\{56 - 60} & {{{{if}\mspace{14mu} 55} < {Clientage} \leq 60}\;;} \\{61 - 65} & {{{{if}\mspace{14mu} 60} < {Clientage} \leq 65}\;;} \\{66 - 70} & {{{{if}\mspace{14mu} 65} < {Clientage} \leq 70}\;;} \\{71 - 75} & {{{{if}\mspace{14mu} 70} < {Clientage} \leq 75}\;;} \\{76 - 80} & {{{{if}\mspace{14mu} 75} < {Clientage} \leq 80}\;;} \\{81 - 85} & {{{{if}\mspace{14mu} 80} < {Clientage} \leq 85}\;;} \\{85 - 90} & {{{{if}\mspace{14mu} 85} < {Clientage} \leq 90}\;;} \\{91 - 95} & {{{{if}\mspace{14mu} 90} < {Clientage} \leq 95}\;;} \\{96 - 100} & {{{{if}\mspace{14mu} 95} < {Clientage} \leq 100}\;;} \\{> 100} & {{{if}\mspace{14mu} {Clientage}} > 100}\end{matrix}.} \right.$

The cumulative probability of dying (E375) is:

${E\; 345} = \left\{ {\begin{matrix}0.005958 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {0 - 1}};} \\0.001021 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {2 - 5}}\;;} \\0.00059 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {6 - 10}}\;;} \\0.000705 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {11 - 15}}\;;} \\0.002227 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {16 - 20}}\;;} \\0.004158 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {21 - 25}}\;;} \\0.004869 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {26 - 30}}\;;} \\0.005727 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {31 - 35}}\;;} \\0.007072 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {36 - 40}}\;;} \\0.009949 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {41 - 45}}\;;} \\0.015604 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {46 - 50}}\;;} \\0.024272 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {51 - 55}}\;;} \\0.035563 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {56 - 60}}\;;} \\0.05006 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {61 - 65}}\;;} \\0.071576 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {66 - 70}}\;;} \\0.109091 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {71 - 75}}\;;} \\0.170567 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {76 - 80}}\;;} \\0.271135 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {81 - 85}}\;;} \\0.425836 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {86 - 90}}\;;} \\0.614587 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {91 - 95}}\;;} \\0.786379 & {{{{if}\mspace{14mu} {Agerange}\; 1} = {96 - 100}}\;;} \\1 & {{{if}\mspace{14mu} {Agerange}\; 1} = {> 100}}\end{matrix}.} \right.$

The life expectancy (E376) for males is calculated below:

${E\; 376} = \left\{ {\begin{matrix}{C\; 2139} & {{{{if}\mspace{14mu} {Clientage}} = 0};} \\{C\; 2140} & {{{{if}\mspace{14mu} 0} < {Clientage} \leq 1};} \\\begin{matrix}{{C\; 2140} - {\left( {{C\; 2140} - {C\; 2141}} \right) \times}} \\{\left( {{Clientage} - 1} \right)/4}\end{matrix} & {{{{if}\mspace{14mu} 1} < {Clientage} \leq 5}\;;} \\\begin{matrix}{{C\; 2141} - {\left( {{C\; 2141} - {C\; 2142}} \right) \times}} \\{\left( {{Clientage} - 5} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 5} < {Clientage} \leq 10}\;;} \\\begin{matrix}{{C\; 2142} - {\left( {{C\; 2142} - {C\; 2143}} \right) \times}} \\{\left( {{Clientage} - 10} \right)/5}\end{matrix} & {{{{if}\mspace{11mu} 10} < {Clientage} \leq 15}\;;} \\\begin{matrix}{{C\; 2143} - {\left( {{C\; 2143} - {C\; 2144}} \right) \times}} \\{\left( {{Clientage} - 15} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 15} < {Clientage} \leq 20}\;;} \\\begin{matrix}{{C\; 2144} - {\left( {{C\; 2144} - {C\; 2145}} \right) \times}} \\{\left( {{Clientage} - 20} \right)/5}\end{matrix} & {{{{if}\mspace{11mu} 20} < {Clientage} \leq 25}\;;} \\\begin{matrix}{{C\; 2145} - {\left( {{C\; 2145} - {C\; 2146}} \right) \times}} \\{\left( {{Clientage} - 25} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 25} < {Clientage} \leq 30}\;;} \\\begin{matrix}{{C\; 2146} - {\left( {{C\; 2146} - {C\; 2147}} \right) \times}} \\{\left( {{Clientage} - 30} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 30} < {Clientage} \leq 35}\;;} \\\begin{matrix}{{C\; 2147} - {\left( {{C\; 2147} - {C\; 2148}} \right) \times}} \\{\left( {{Clientage} - 35} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 35} < {Clientage} \leq 40}\;;} \\\begin{matrix}{{C\; 2148} - {\left( {{C\; 2148} - {C\; 2149}} \right) \times}} \\{\left( {{Clientage} - 40} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 40} < {Clientage} \leq 45}\;;} \\\begin{matrix}{{C\; 2149} - {\left( {{C\; 2149} - {C\; 2150}} \right) \times}} \\{\left( {{Clientage} - 45} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 45} < {Clientage} \leq 50}\;;} \\\begin{matrix}{{C\; 2150} - {\left( {{C\; 2150} - {C\; 2151}} \right) \times}} \\{\left( {{Clientage} - 50} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 50} < {Clientage} \leq 55}\;;} \\\begin{matrix}{{C\; 2151} - {\left( {{C\; 2151} - {C\; 2152}} \right) \times}} \\{\left( {{Clientage} - 55} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 55} < {Clientage} \leq 60}\;;} \\\begin{matrix}{{C\; 2152} - {\left( {{C\; 2152} - {C\; 2153}} \right) \times}} \\{\left( {{Clientage} - 60} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 60} < {Clientage} \leq 65}\;;} \\\begin{matrix}{{C\; 2153} - {\left( {{C\; 2153} - {C\; 2154}} \right) \times}} \\{\left( {{Clientage} - 65} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 65} < {Clientage} \leq 70}\;;} \\\begin{matrix}{{C\; 2154} - {\left( {{C\; 2154} - {C\; 2155}} \right) \times}} \\{\left( {{Clientage} - 70} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 70} < {Clientage} \leq 75}\;;} \\\begin{matrix}{{C\; 2155} - {\left( {{C\; 2155} - {C\; 2156}} \right) \times}} \\{\left( {{Clientage} - 75} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 75} < {Clientage} \leq 80}\;;} \\\begin{matrix}{{C\; 2156} - {\left( {{C\; 2156} - {C\; 2157}} \right) \times}} \\{\left( {{Clientage} - 80} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 80} < {Clientage} \leq 85}\;;} \\\begin{matrix}{{C\; 2157} - {\left( {{C\; 2157} - {C\; 2158}} \right) \times}} \\{\left( {{Clientage} - 85} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 85} < {Clientage} \leq 90}\;;} \\\begin{matrix}{{C\; 2158} - {\left( {{C\; 2158} - {C\; 2159}} \right) \times}} \\{\left( {{Clientage} - 90} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 90} < {Clientage} \leq 95}\;;} \\\begin{matrix}{{C\; 2159} - {\left( {{C\; 2159} - {C\; 2160}} \right) \times}} \\{\left( {{Clientage} - 95} \right)/5}\end{matrix} & {{{{if}\mspace{14mu} 95} < {Clientage} \leq 100}\;;} \\{C\; 2160} & {{{if}\mspace{14mu} {Clientage}} > 100}\end{matrix}.} \right.$

where C2139-C2160 refer to the cells of FIG. 22, which contains exampledata regarding the life expectancy of the general population atdifferent ages. As above, general population data may be continuouslyand automatically collected from a variety of sources including, withoutlimitation, the Canadian National Vital Statistics Reports, Vol 64 No,2, Feb. 16, 2016. To calculate life expectancy for females, the samecalculations as above are performed with references to column “C”replaced by column “D”.

The overall mortality rate per 100,000 individuals (E379) is:

${E\; 379} = \left\{ {\begin{matrix}{{{I\; 2090\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{0 - {1\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2091\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{2 - {4\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2092\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{5 - {1\; 4\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 209\; 3\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{15 - {24\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2094\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{25 - {34\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2095\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{35 - {44\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2096\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{45 - {54\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2097\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{55 - {64\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2098\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{65 - {74\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2099\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{75 - {84\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\{{{I\; 2100\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{>={85\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = M}};} \\\begin{matrix}{{{J\; 2090\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{0 - {1\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2091\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{2 - {4\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2092\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{5 - {1\; 4\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 209\; 3\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{15 - {24\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2094\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{25 - {34\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2095\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{35 - {44\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2096\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{45 - {54\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2097\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{55 - {64\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2098\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{65 - {74\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{{J\; 2099\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{75 - {84\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}};} \\{{J\; 2100\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {{>={85\mspace{14mu} {and}\mspace{14mu} {ClientGender}}} = F}}\end{matrix}\end{matrix}.} \right.$

where I2090-I2100 and J2090-J2100 refer to the cells of FIG. 23, whichcontains data regarding the mortality rate of the general population per100,000 individuals, as obtained from the Canadian National VitalStatistics Reports, Vol 64 No, 2, Feb. 16, 2016.

Agerange2 can be specified as follows:

${{Agerange}\; 2} = \left\{ {\begin{matrix}{0 - 1} & {{{{if}\mspace{14mu} {Clientage}} \leq 1};} \\{2 - 4} & {{{{if}\mspace{14mu} 1} < {Clientage} \leq 4};} \\{5 - 14} & {{{{if}\mspace{14mu} 4} < {Clientage} \leq 14};} \\{15 - 24} & {{{{if}\mspace{14mu} 14} < {Clientage} \leq 24};} \\{25 - 34} & {{{{if}\mspace{14mu} 24} < {Clientage} \leq 34};} \\{35 - 44} & {{{{if}\mspace{14mu} 34} < {Clientage} \leq 44};} \\{45 - 54} & {{{{if}\mspace{14mu} 44} < {Clientage} \leq 54};} \\{55 - 64} & {{{{if}\mspace{14mu} 54} < {Clientage} \leq 64};} \\{65 - 74} & {{{{if}\mspace{14mu} 64} < {Clientage} \leq 74};} \\{75 - 84} & {{{{if}\mspace{14mu} 74} < {Clientage} \leq 84};} \\{>=85} & {{{if}\mspace{14mu} {Clientage}} \geq 85}\end{matrix}.} \right.$

Mortality Rate Due to Cardiovascular Disease

As another example, certain risks associated with the mortality ofcardiovascular diseases can be generated based on the client's age(Clientage), client's gender (ClientGender), client's BMI (ClientBMI),client's daily average steps (ClientStepAvgActi), client's medicaldiagnosis on cardiovascular diseases (ClientCarDis), and the effect oftreatment on the client's cardiovascular disease (CardioSitu), whereingeneral population distribution data regarding average daily steps, BMI,life expectancy, probabilities of dying, and mortality rates are alsoconsidered. Logistic models can be used to predict mortality due tocardiovascular disease using the above incoming wellness information,and the results can be tabulated and stored as, for example, shown inFIGS. 22 and 23, showing life expectancy and mortality per 100,000individuals, respectively, as well as FIG. 24 showing the probabilitiesof dying for various age ranges. Using such general populationinformation, mortality rate statistics can be calculated such as,without limitation, the following: Mortality rate per 100,000individuals (mortHD) for people with heart diseases in the client's agerange (Agerange2); Mortality rate due to heart disease based on averagedaily steps for client (E384); Mortality rate due to heart disease basedon average daily steps for client and client's gender (F384); Mortalityrate due to heart disease based on average daily steps for client andclient's age and gender (G384); Mortality rate due to heart diseasebased on client's daily steps and BMI (E385); Mortality rate due toheart disease based on daily steps, BMI and client's gender (F385);Mortality rate due to heart disease based on daily steps, BMI andclient's age and gender (G385). First, the AvgSteps, AvgStepGender,AvgStepGenAge, ClientBMIGender, and ClientBMIGenAge are calculated.

AvgSteps = ClientStepAvgActi ${AvgStepGender} = \left\{ {\begin{matrix}{9070 + {Adjust}} & {{{{if}\mspace{14mu} {ClientGender}} = M};} \\{7779 + {Adjust}} & {{{{if}\mspace{14mu} {ClientGender}} = F};} \\{8419 + {Adjust}} & {{{if}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix},} \right.$

where the above are the mean step counts for each gender overall.

${AvgStepGenAge} = \left\{ {\begin{matrix}{{{9224 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}}\;;} \\{{{8830 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}}\;;} \\{{{8941 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}}\;;} \\{{{8264 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}}\;;} \\{{{7368 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}}\;;} \\{{{6237 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\;;} \\{{{9848 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}}\;;} \\{{{9422 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}}\;;} \\{{{9837 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}}\;;} \\{{{8687 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}}\;;} \\{{{7878 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}}\;;} \\{{{6906 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\;;} \\{{{8534 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}}\;;} \\{{{8280 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}}\;;} \\{{{8010 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}}\;;} \\{{{7867 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}}\;;} \\{{{6880 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}}\;;} \\{{8677 + {{Adjust}\mspace{14mu} {if}\mspace{14mu} {ClientGender}}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\end{matrix};} \right.$

where the above are mean step counts for each age bracket and gender.

${ClientBMIGender} = \left\{ {\begin{matrix}28 & {{{{if}\mspace{14mu} {ClientGender}} = M};} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = F};} \\28 & {{{if}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix},} \right.$

where the above are the mean BMI values for each gender overall.

${ClientBMIGenAge} = \left\{ {\begin{matrix}26 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}}\;;} \\27 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}}\;;} \\29 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}}\;;} \\29 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}}\;;} \\29.23 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}}\;;} \\27 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}}\;;} \\28 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}}\;;} \\27 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\end{matrix};} \right.$

where the above are the mean BMI values for each age bracket and gender.

The mortality rate statistics above can then be calculated as, forexample:

1. The mortality rate (mortHD) for people with heart disease is givenby:

${mortHD} = \left\{ {\begin{matrix}{{{K\; 2090\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {0 - 1}};} \\{{{K\; 2091\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {2 - 4}};} \\{{{K\; 2092\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {5 - {1\; 4}}};} \\{{{K\; 209\; 3\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {15 - 24}};} \\{{{K\; 2094\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {25 - 34}};} \\{{{K\; 2095\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {35 - 44}};} \\{{{K\; 2096\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {45 - 54}};} \\{{{K\; 2097\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {55 - 64}};} \\{{K\; 2098\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {65 - 74}} \\{{{K\; 2099\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {75 - 84}};} \\{{K\; 2100\mspace{14mu} {if}\mspace{14mu} {Agerange}\; 2} = {>=85}}\end{matrix}.} \right.$

where K2090-K2100 refer to the cells of FIG. 23.

2. The mortality rate (E384) due to heart diseases based on averagedaily steps of the client is given by:

${E\; 384} = {{{MCS}\mspace{11mu} \left( {{AvgSteps},{mortClientCardio},{ClientGender}} \right)} = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {mortClientCardio}} = {FALSE}};} \\{mortHD} & {{{if}\mspace{14mu} {mortClientCardio}} = Y} \\\begin{matrix}{{mortHD} \times {logistic}} \\\left( {{CSIntM} + {{CSStM} \times {AvgSteps}}} \right)\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {mortClientCardio}} = N} \\{{{{and}\mspace{14mu} {ClientGender}} = M};}\end{matrix} \\\begin{matrix}{{mortHD} \times {logistic}} \\\left( {{CSIntF} + {{CSStF} \times {AvgSteps}}} \right)\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {mortClientCardio}} = N} \\{{{and}\mspace{14mu} {ClientGender}} = F}\end{matrix}\end{matrix}.} \right.}$

where MCS( , , ) denotes E384 as a function of AvgSteps,mortClientCardio, and ClientGender.

3. Similarly, the mortality rate (F384) due to heart diseases based onaverage daily steps for the client's gender is given by:

F384=MCS(AvgStepGender, mortClientCardio, ClientGender)

4. The mortality rate (G384) due to heart diseases based on the averagedaily steps for the client's gender and age is given by:

G384=MCS(AvgStepGenAge, mortClientCardio, ClientGender)

5. The mortality rate (E385) due to heart diseases based on the averagedaily steps and BMI of the client is given by:

${E\; 385} = {{{MCSB}\left( {{AvgSteps},{ClientBMI},{mortClientCardio},{ClientGender}} \right)} = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {mortClientCardio}} = {FALSE}};} \\{mortHD} & {{{if}\mspace{14mu} {mortClientCardio}} = Y} \\\begin{matrix}\begin{matrix}{{mortHD} \times {{logistic}\left( {{CSBIntM} +} \right.}} \\{{{CSBStM} \times {AvgSteps}} + {{CSBbmiM} \times}}\end{matrix} \\{{ClientBMI} + {{CSBStbmiM} \times}} \\\left. {{AvgSteps} \times {ClientBMI}} \right)\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {mortClientCardio}} = N} \\{{{{and}\mspace{14mu} {ClientGender}} = M};} \\\; \\\;\end{matrix} \\\begin{matrix}\begin{matrix}{{mortHD} \times {{logistic}\left( {{CSBIntF} +} \right.}} \\{{{CSBStF} \times {AvgSteps}} + {{CSBbmiF} \times}}\end{matrix} \\{{ClientBMI} + {{CSBStbmiF} \times}} \\\left. {{AvgSteps} \times {ClientBMI}} \right)\end{matrix} & \begin{matrix}{{{if}\mspace{14mu} {mortClientCardio}} = N} \\{{{and}\mspace{14mu} {ClientGender}} = F} \\\; \\\;\end{matrix}\end{matrix}.} \right.}$

where MCSB( , , , ) denotes E385 as a function of AvgSteps, ClientBMI,mortClientCardio, and ClientGender.

6. Similarly, the mortality rate (F385) due to heart diseases based onaverage daily steps and BMI for the client's gender is given by:

F385=MCSB(AvgStepGender, ClientBMIGender, mortClientCardio,ClientGender)

7. The mortality rate (G385) due to heart diseases based on averagedaily steps and BMI for the client's gender and age is given by:

G385=MCSB(AvgStepGenAge, ClientBMIGenAge, mortClientCardio,ClientGender)

Mortality Rates of Diabetes

As another example, certain risks associated with mortality due todiabetes can be calculated based on the client's age (Clientage),client's gender (ClientGender), client's BMI (ClientBMI), client's dailyaverage steps (ClientStepAvgActi), client's medical diagnosis onabnormal blood pressure (ClientBPRDis), the effect of treatment on theclient's abnormal blood pressure (BPRSitu), client's medical diagnosison diabetes (ClientDiaDis), the effect of treatment on the client'sdiabetes (DiabeSitu), and client's family history of diabetes(ClientDiaFamily), whereby population distribution data regarding dailyaverage steps, BMI, waist size, life expectancy, probabilities of dying,and mortality rates are also provided. Using the above data, withoutlimitation, one ore more mortality rate statistics can be calculatedincluding: Mortality rate per 100,000 individuals (mortDia) for peoplewith diabetes in the client's age range (Agerange4) Mortality rate dueto diabetes based on average daily steps, BMI, MV, waist size,ClientBPR, and ClientDiaFamiliy for client (E439); Mortality rate due todiabetes based on average daily steps, BMI, MV, waist size, ClientBPR,and ClientDiaFamiliy, and client gender (F439); Mortality rate due todiabetes based on average daily steps, BMI, MV, waist size, ClientBPR,and ClientDiaFamiliy, and client age and gender (G439). First, theAvgMVGenActi, AvgMVGenAgeActi, AvgWaistGen, and AvgWaistGenAge arecalculated.

${AvgMVGenActi} = \left\{ {\begin{matrix}22.261982 & {{{{if}\mspace{14mu} {ClintGender}} = M};} \\17.692627 & {{{{if}\mspace{14mu} {ClientGender}} = F};} \\19.933881 & {{{if}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix},} \right.$

where the above are the mean MV values for each gender overall.

${AvgMVGenAgeActi} = \left\{ {\begin{matrix}27.261345 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\22.877719 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\21.183897 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\18.057031 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\13.165338 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\9.948715 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\29.981944 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\25.991409 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\24.419647 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\18.855232 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\13.874513 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\11.686853 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\24.308956 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\20.014397 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\17.935525 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\17.30886 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\12.49993 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\8.529676 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\end{matrix}.} \right.$

where the above are mean MV values for each age bracket and gender.

${AvgWaistGen} = \left\{ {\begin{matrix}96.43 & {{{{if}\mspace{14mu} {ClientGender}} = M};} \\88.36 & {{{{if}\mspace{14mu} {ClientGender}} = F};} \\92.38 & {{{if}\mspace{14mu} {ClientGender}} = {NA}}\end{matrix},} \right.$

where the above are mean values for waist size for each gender overall.

${AvgWaistGenAge} = \left\{ {\begin{matrix}85.53 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\89.95 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\92.88 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\95.34 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\97.38 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\96.39 & {{{{if}\mspace{14mu} {ClientGender}} = {{{NA}\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\87.24 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\94 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\96.85 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\101.26 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\102.6 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\101.57 & {{{{if}\mspace{14mu} {ClientGender}} = {{M\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}};} \\83.61 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} < 30}};} \\85.98 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 30} \leq {Clientage} < 40}};} \\88.76 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 40} \leq {Clientage} < 50}};} \\89.79 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 50} \leq {Clientage} < 60}};} \\92.45 & {{{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} 60} \leq {Clientage} < 70}};} \\92.05 & {{{if}\mspace{14mu} {ClientGender}} = {{F\mspace{14mu} {and}\mspace{14mu} {Clientage}} \geq 70}}\end{matrix};} \right.$

where the above are mean values for waist size for each age bracket andgender.

The mortality rate statistics above can then be calculated:

1. The mortality rate (mortDia) for people with diabetes is given by:

${mortDia} = \left\{ {\begin{matrix}{{{L\; 2090\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {0 - 1}};} \\{{{L\; 2091\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {2 - 4}};} \\{{{L\; 2092\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {5 - 14}};} \\{{{L\; 2093\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {15 - 24}};} \\{{{L\; 2094\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {25 - 34}};} \\{{{L\; 2095\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {35 - 44}};} \\{{{L\; 2096\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {45 - 54}};} \\{{{L\; 2097\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {55 - 64}};} \\{{{L\; 2098\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {65 - 74}};} \\{{{L\; 2099\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {75 - 84}};} \\{{L\; 2100\mspace{14mu} {if}\mspace{11mu} {Agerange}\; 2} = {>=85}}\end{matrix}.} \right.$

where L2090-L2100 refer to the cells of FIG. 23.

2. The mortality rate (E439) due to diabetes based on average dailysteps, BMI, MV, waist size, blood pressure situation (BPRSitu) andfamily history of diabetes (ClientDiaFamily) is given by:

${E\; 439} = \left\{ {\begin{matrix}{mortDia} & {{{{if}\mspace{14mu} {ClientDiabetes}} = Y};} \\{{mortDia} \times {DAvgRisk}} & {otherwise}\end{matrix}.} \right.$

where DAvgRisk=(RDia1+RDia2+RDia3)/3 and

RDia1=DSBF(ClientStepAvgActi, ClientBMI, ClientDiaFamily, ClientGender);

RDia2=DMBP(ClientMVAvgActi, ClientBMI, ClientBPR, ClientGender);

RDia3=DW(ClientSWaist, ClientGender);

3. Similarly, the mortality rate (F439) due to diabetes based on averagedaily steps, BMI, MV, waist size, blood pressure situation (BPRSitu),family history of diabetes (ClientDiaFamily), and client's gender isgiven by:

${F\; 439} = \left\{ {\begin{matrix}{mortDia} & {{{{if}\mspace{14mu} {ClientDiabetes}} = Y};} \\{{mortDia} \times {DAvgRiskGender}} & {otherwise}\end{matrix}.} \right.$

where DAvgRiskGender=(RDiaGen1+RDiaGen2+RDiaGen3)/3 and

RDiaGen1=DSBF(AvgStepGender,ClientBMIGender, ClientDiaFamily,ClientGender);

RDiaGen2=DMBP(AvgMVGenActi, ClientBMIGender, ClientBPR, ClientGender);

RDiaGen3=DW(AvgWaistGen, ClientGender);

4. The mortality rate (G439) due to diabetes based on average dailysteps, BMI, MV, waist size, blood pressure situation (BPRSitu), familyhistory of diabetes (ClientDiaFamily), and client's gender is given by:

${G\; 439} = \left\{ {\begin{matrix}{mortDia} & {{{{if}\mspace{14mu} {ClientDiabetes}} = Y};} \\{{mortDia} \times {DAvgRiskGenAge}} & {otherwise}\end{matrix}.} \right.$

Financial Implications

As above, the present systems may be further utilized to estimate orpredict the costs of various diseases and savings that could beassociated with various behavioral changes or changes in personalcharacteristics, such as increased physical activity or decreases inweight, providing the advantage that the costs or financial implicationsof an individual's or group's overall wellness can be estimated orpredicted, and improved. By way of example, a financial HRA can becalculated to provide useful statistics regarding the cost of, withoutlimitation, cardiovascular diseases, diabetes, etc.

Cost of Cardiovascular Diseases

By way of example, certain metrics relating to the cost ofcardiovascular diseases can be generated based upon, without limitation,the following incoming wellness information: Age, Gender, Client'scurrent BMI, Client's daily average steps, Client's daily average MVactivity time in minutes, Whether client's blood pressure is abnormal,Whether treatment helps client's abnormal blood pressure, Client'smedical diagnosis on cardiovascular diseases, Whether treatment helpsclient's cardiovascular disease, and the client's geo-location (i.e.which Province the Client lives in). As above, general population dataregarding steps, MV, BMI, and waist can be used as a baseline with whichto compare the client. Additionally, the provincial average annual costper person on cardiovascular diseases is provided. Certain metrics ofcardiovascular disease cost can be calculated, such as: Average annualcost per person on cardiovascular diseases for client withcardiovascular disease (1730); Average annual cost per person oncardiovascular diseases for client (1732); Average annual cost perperson on cardiovascular diseases for client's gender group(CostCardioClientGen); and Average annual cost per person oncardiovascular diseases for client's gender/age group(CostCardioClientAGen). The metrics can be calculated as follows:

1. The average annual cost per person on cardiovascular disease forclient with cardiovascular diseases 1730 is simply the number reportedon the provincial report of the cost of cardiovascular diseases in theprovince.

$1730 = \left\{ {\begin{matrix}18513 & {{{{if}\mspace{14mu} {Location}} = {BC}};} \\18513 & {{{{if}\mspace{14mu} {Location}} = {AB}};} \\18513 & {{{{if}\mspace{14mu} {Location}} = {SK}};} \\18513 & {{{{if}\mspace{14mu} {Location}} = {MB}};} \\18513.4 & {{{{if}\mspace{14mu} {Location}} = {ON}};} \\18513.4 & {{{{if}\mspace{14mu} {Location}} = {NB}};} \\18513 & {{{{if}\mspace{14mu} {Location}} = {PE}};} \\18513.38 & {{{{if}\mspace{14mu} {Location}} = {NS}};} \\18513.4 & {{{{if}\mspace{14mu} {Location}} = {NL}};} \\18513.38 & {{{if}\mspace{14mu} {Location}} = {QC}} \\\; & {{{or}\mspace{14mu} {Location}} = {Unknown}}\end{matrix}.} \right.$

2. The average annual cost per person on diabetes for the client 1732 isgiven by:

$1732 = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {ClientCardio}} = {FALSE}}\;;} \\1730 & {{{{if}\mspace{14mu} {ClientCardio}} = Y};} \\{1730 \times {CAvgRisk}} & {otherwise}\end{matrix}.} \right.$

3. The average annual cost per person on diabetes for client's gendergroup CostCardioClientGen is given by:

${CostCardioClientGen} = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {ClientCardio}} = {FALSE}}\;;} \\1730 & {{{{if}\mspace{14mu} {ClientCardio}} = Y};} \\{1730 \times {AvgRickCardioGenHel}} & {otherwise}\end{matrix},} \right.$

where AvgRickCardioGenHel is calculated by the following formula:

AvgRickCardioGenHel=(CSBF(AvgStepGenActi, ClientBMIGender,ClientCarFamily=N)+CMBF(AvgMVGenActi,ClientBMIGender,ClientCarFaimly=N)+CSP(AvgStepGenActi, ClientBPR=N,ClientGender)+CBP(ClientBMIGender, ClientBPR=N,ClientGender)+CMP(AvgMVGenActi, ClientBPR=N,ClientGender)+CW(AvgWaistGen, ClientGender))/6,

and AvgStepGenActi=AvgStepGender

4. The average annual cost per person on diabetes for client'sgender/age group CostCardioClientAGen is given by:

${CostCardioClientAGen} = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {ClientCardio}} = {FALSE}}\;;} \\1730 & {{{{if}\mspace{14mu} {ClientCardio}} = Y};} \\{1730 \times {AvgRickCardioGenAgeHel}} & {otherwise}\end{matrix}.} \right.$

where AvgRickCardioGenAgeHel is calculated by the following formula:

AvgRickCardioGenHel=(CSBF(AvgStepGenAgeActi, ClientBMIGenAge,ClientCarFaimly=N)+CMBF(AvgMVGenAgeActi, ClientBMIGenAge,ClientCarFamily=N)+CSP(AvgStepGenAgeActi, ClientBPR=N,ClientGender)+CBP(ClientBMIGenAge, ClientBPR=N,ClientGender)+CMP(AvgMVGenAgeActi, ClientBPR=N,ClientGender)+CW(AvgWaistGenAge, ClientGender))/6,

Cost of Diabetes

As another example, certain metrics relating to the cost of diabetes canbe generated based on the following inputs: Age, Gender, Client'scurrent BMI, Client's daily average steps, Client's daily average MVactivity time in minutes, Whether client's blood pressure is abnormal,Whether treatment helps client's abnormal blood pressure, Client'smedical diagnosis on diabetes, Whether treatment helps client'sdiabetes, Client's family history shows diabetes, and the geo-location(i.e. the province that Client lives in). As above, population dataregarding steps, MV, BMI, and waist can be used as a baseline with whichto compare the client. Additionally, the provincial average annual costper person on diabetes is provided. Certain metrics of diabetes costcan, without limitation, be calculated including: Average annual costper person on diabetes for client with diabetes (1737); Average annualcost per person on diabetes for client (1739); Average annual cost perperson on diabetes for client's gender group (CostDiaClientGen); andAverage annual cost per person on diabetes for client's gender/age group(CostDiaClientAGen). Certain metrics can be calculated as follows:

1. The average annual cost per person on diabetes for client withdiabetes 1737 is simply the number reported on the provincial report ofthe cost of diabetes in the province:

$1737 = \left\{ {\begin{matrix}3717.1 & {{{{if}\mspace{14mu} {Location}} = {BC}};} \\4746 & {{{{if}\mspace{14mu} {Location}} = {AB}};} \\5286.5 & {{{{if}\mspace{14mu} {Location}} = {SK}};} \\4992.1 & {{{{if}\mspace{14mu} {Location}} = {MB}};} \\3954.05 & {{{{if}\mspace{14mu} {Location}} = {ON}};} \\4190.17 & {{{{if}\mspace{14mu} {Location}} = {NB}};} \\4850 & {{{{if}\mspace{14mu} {Location}} = {PE}};} \\4203.523 & {{{{if}\mspace{14mu} {Location}} = {NS}};} \\5018.31 & {{{{if}\mspace{14mu} {Location}} = {NL}};} \\4880 & {{{if}\mspace{14mu} {Location}} = {QC}} \\\; & {{{or}\mspace{14mu} {Location}} = {Unknown}}\end{matrix}.} \right.$

2. The average annual cost per person of diabetes for client 1739 isgiven by:

$1739 = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {ClientDiabetes}} = {FALSE}}\;;} \\1737 & {{{{if}\mspace{14mu} {ClientDiabetes}} = Y};} \\{1737 \times {DAvgRisk}} & {otherwise}\end{matrix}.} \right.$

3. The average annual cost per person of diabetes for client's gendergroup CostDiaClientGen is given by:

${CostDiaClientGen} = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {ClientDiabetes}} = {FALSE}}\;;} \\1737 & {{{{if}\mspace{14mu} {ClientDiabetes}} = Y};} \\{1737 \times {AvgRiskDiabGenHel}} & {otherwise}\end{matrix}.} \right.$

where AvgRiskDiabGenHel is calculated by the following formula:

AvgRickDiabGenHel=(DSBF(AvgStepGenActi, ClientBMIGender,ClientDiaFamily=N, ClientGender)+DMBP(AvgMVGenActi, ClientBMIGender,ClientBPR=N, ClientGender)+DW(AvgWaistGen, ClientGender))/3,

4. The average annual cost per person of diabetes for client'sgender/age group CostDiaClientAGen is given by:

${CostDiaClientAGen} = \left\{ {\begin{matrix}{FALSE} & {{{{if}\mspace{14mu} {ClientDiabetes}} = {FALSE}}\;;} \\1737 & {{{{if}\mspace{14mu} {ClientDiabetes}} = Y};} \\{1737 \times {AvgRiskDiabGenAgeHel}} & {otherwise}\end{matrix},} \right.$

where AvgRiskDiabGenAgeHel is calculated by the following formula:

AvgRickDiabGenHel=(DSBF(AvgStepGenAgeActi, ClientBMIGenAge,ClientDiaFamily=N, ClientGender)+DMBP(AvgMVGenAgeActi, ClientBMIGenAge,ClientBPR=N, ClientGender)+DW(AvgWaistGenAge, ClientGender))/3,

The terms and expressions herein are used as terms of description andnot as limitation. Although the particular embodiments of the presentsystems described have been illustrated in the foregoing detaileddescription, it is to be further understood that the present inventionis not to be limited to just the embodiments disclosed, but that theyare capable of numerous rearrangements, modifications, andsubstitutions.

We claim:
 1. A computer-implemented method for determining wellness ofan individual, the method comprising: providing a processor, inelectronic communication with at least one or more device adapted toreceive and transmit specific incoming wellness information about theindividual, providing a general population information database, inelectronic communication with the processor, for receiving andtransmitting general population information to the processor, andreceiving, at the processor, the specific incoming wellness informationabout the individual from the at least one or more devices and thegeneral population information from the general population informationdatabase, and processing the specific wellness information and thegeneral population information to generate at least one digitalbiomarker subscore indicative of the individual's wellness according tothe specific wellness information, as compared against the generalpopulation information, and generating output information of the atleast one digital biomarker subscore and transmitting the outputinformation to the at least one or more devices.
 2. The method of claim1, wherein the specific wellness information comprises physical,behavioral, emotional, social, demographic and/or environmentalinformation about the individual.
 3. The method of claim 1, wherein thespecific wellness information comprises at least age, gender, height andweight, waist circumference, physical activity, minutes ofmoderate/vigorous activity, sleep patterns, smoking habits, drug andalcohol consumption, nutrition, family history, pain, stress andhappiness levels, resting heart rate, exercise heart rate, heart ratevariability, presence of pre-existing disease, job type, geo-location,EEG, voice data, breathing data, blood biometrics, body composition(DXA), and aerobic fitness (VO2 max).
 4. The method of claim 1, whereinthe digital biomarker subscores may be indicative of health behaviors,chronic disease risk, mental health or mortality.
 5. The method of claim5, wherein the health behaviors may comprise information about, atleast, steps taken per day, moderate to vigorous activity levels, sleeppatterns, body mass index, waist circumference, smoking habits, drinkinghabits, nutritional habits, and aerobic fitness.
 6. The method of claim5, wherein the disease risk may comprise information about, at least,cardiovascular disease, diabetes, arthritis, lung disease, and pain. 7.The method of claim 5, wherein the mental health subscore may provideinformation about, at least, stress levels, happiness levels,depression, and model-based happiness.
 8. The method of claim 5, whereinthe mortality subscore may be determined utilizing informationcomprising age, risk of cardiovascular disease, and risk of diabetes. 9.The method of claim 1, wherein the digital biomarker subscores may begenerated in an interactive manner, wherein the individual may predictor estimate how changes to one or more of the digital biomarkersubscores changes their wellness.
 10. The method of claim 1, wherein thedigital biomarker subscores may be generated in an interactive manner,wherein the individual may observe the digital biomarker subscores ofother individuals or groups of individuals for interaction therewith.11. The method of claim 1, wherein the method may be utilized toestimate or predict financial implications of the individual's wellness.12. The method of claim 1, wherein the wellness information may beutilized to create and optimize health-related programs and products,insurance programs and products, and wellness support programs andproducts.
 13. The method of claim 1, wherein the method furthercomprises the processing of one or more of the at least one digitalbiomarker subscores against further general population information togenerate an overall wellness score for the individual.
 14. The method ofclaim 1, wherein the method may be utilized to determine the wellness ofa group of individuals.
 15. A computer-implemented system fordetermining the wellness of an individual, the system comprising: atleast one device adapted to receive and transmit incoming wellnessinformation about the individual, at least one general populationdatabase, operative to receive and transmit incoming wellnessinformation from the at least one device and at least one processor, andat least one processor, in electronic communication with the at leastone device and the general population database, the processor operativeto receive the incoming wellness information from the at least onedevice and the general population information from the database, and toprocess the information to generate at least one digital biomarkersubscore indicative of the individual's wellness according to thespecific incoming wellness information as compared against the generalpopulation information, and to generate at least one output indicativeof the at least one digital biomarker subscore and transmitting theoutput to the at least one device.
 16. The system of claim 15, whereinthe incoming wellness information and the general population informationare transmitted via wired or wireless signaling.
 17. The system of claim15, wherein the incoming wellness information is received andtransmitted by the at least one device automatically, manually, or acombination thereof.
 18. The system of claim 15, wherein the incomingwellness information is received and transmitted by the at least onedevice intermittently, continuously, or a combination thereof.
 19. Thesystem of claim 15, wherein the at least one device may comprise, atleast, any device having a user interface, cloud computing, orapplication program interfaces.
 20. The system of claim 19, wherein theat least one device may comprise one or more wearable device.